Drug Metabolism
GUIDE TO INNOVATION
For Research Use Only. Not for use in diagnostic procedures.
Drug Metabolism – Analysis with Confidence
Pharmaceutical and biopharmaceutical companies have leveraged
advancements in basic science perhaps more than any other industry.
With the advent of whole genome sequencing, sophisticated analysis
of metabolic pathways, and exponential improvements in computer
processing, R&D organizations have expanded their drug portfolio focus
from small molecules to biotherapeutics.
To ensure continued success in drug development, the Industry has been
challenged to design and deliver targeted new medicines with higher
efficiency at a faster pace than ever before. As a result, better and faster
information is required to develop a fundamental understanding of the
target compound’s efficiency as a therapeutic and its metabolic profile
for potential toxicity. Partnering with customers by understanding their
challenges, collaborating on ideas, and ultimately creating cutting-edge
solutions that provide new ways to address these new challenges in
drug metabolism is our priority. The following compendium includes
key solutions for drug metabolism — and, more importantly, describes
in detail work done by, and in collaboration with, our customers. Your
success is our success, and the AB SCIEX team will partner with you to
overcome some of the biggest challenges of drug metabolism analysis,
now and into the future.
Gary Impey, PhD
Director, Pharma/CRO MS Business
RUO-MKT-01-1583-A
Warrington,UK
Redwood Shores &
Dublin, CA, U.S.A.
Concord, ONT,Canada
Paris, France
Framingham, MA, U.S.A.
Darmstadt, Germany
Milan, Italy
Tokyo, Japan
Seoul, S. Korea
New Delhi, India
Shanghai, China
Singapore
Melbourne, Australia
AB SCIEX global associates and sites
1,500+ associates worldwide
260+ hold PhDs or other advanced degrees
14 operating sites
Global sales and service teams
7 global demo labs
For Research Use Only. Not for use in diagnostic procedures.
Drug Metabolism – Analysis with Confidence
Pharmaceutical and biopharmaceutical companies have leveraged
advancements in basic science perhaps more than any other industry.
With the advent of whole genome sequencing, sophisticated analysis
of metabolic pathways, and exponential improvements in computer
processing, R&D organizations have expanded their drug portfolio focus
from small molecules to biotherapeutics.
To ensure continued success in drug development, the Industry has been
challenged to design and deliver targeted new medicines with higher
efficiency at a faster pace than ever before. As a result, better and faster
information is required to develop a fundamental understanding of the
target compound’s efficiency as a therapeutic and its metabolic profile
for potential toxicity. Partnering with customers by understanding their
challenges, collaborating on ideas, and ultimately creating cutting-edge
solutions that provide new ways to address these new challenges in
drug metabolism is our priority. The following compendium includes
key solutions for drug metabolism — and, more importantly, describes
in detail work done by, and in collaboration with, our customers. Your
success is our success, and the AB SCIEX team will partner with you to
overcome some of the biggest challenges of drug metabolism analysis,
now and into the future.
Gary Impey, PhD
Director, Pharma/CRO MS Business
RUO-MKT-01-1583-A
Warrington,UK
Redwood Shores &
Dublin, CA, U.S.A.
Concord, ONT,Canada
Paris, France
Framingham, MA, U.S.A.
Darmstadt, Germany
Milan, Italy
Tokyo, Japan
Seoul, S. Korea
New Delhi, India
Shanghai, China
Singapore
Melbourne, Australia
AB SCIEX global associates and sites
1,500+ associates worldwide
260+ hold PhDs or other advanced degrees
14 operating sites
Global sales and service teams
7 global demo labs
For Research Use Only. Not for use in diagnostic procedures.
Xenobiotic Metabolism Workflows in
Drug Discovery and Development
Introduction
6-15
Contents
Xenobiotic Metabolism Workflows in Drug Discovery and Development
RUO-MKT-01-1583-A
Technology Spotlight
16-27
Technology Drives High Performance in Biomolecular Mass Spectrometry
16-19
High Resolution Time-of-Flight MS
20-23
PCVG: A Powerful Algorithm for Automating Comprehensive Xenobiotic
Metabolite Identification
24-27
Early Discovery
28-43
DiscoveryQuant™ Software 2.0: The Definitive Solution for LC/MS/MS
Early-ADME Workflows
28-31
Confirmation of In Vitro Nefazodone Metabolites Using the Superior
Fragmentation of the QTRAP® 5500 LC/MS/MS System
32-37
Breakthrough Productivity for ADME Studies Using the
AB SCIEX TripleTOF® 5600 System
38-43
44-67
Late Stage Discovery
44-47
Rapid Metabolite Identification Using MetabolitePilot™ Software
and the TripleTOF® 5600 System
48-51
In Vivo Metabolic Profiling of Carbamazepine Using the QTRAP® 5500 System
and LightSight® Software 2.2
52-57
Comprehensive Detection of Metabolites Using Polarity Switching Data
Collection with the QTRAP® 5500 LC/MS/MS System
58-61
Simultaneous Pharmacokinetic Profiling and Automated Metabolite
Identification Using the AB SCIEX TripleTOF™ 5600 System
and MetabolitePilot™ Software
62-67
Metabolite Identification with the QTRAP® 5500 LC/MS/MS System:
Sensitivity, Selectivity, Speed, and Unique Workflows
68-85
Definitive Metabolite ID
68-71
Simultaneous Metabolite Identification and Quantitation with
UV Data Integration Using LightSight® Software 2.2
72-77
Differential Mobility Spectrometry for Quantitative and Qualitative Applications
in Pharmaceutical Workflows
78-81
Solving Bottlenecks in Metabolite Identification Using TripleTOF® Systems
and MetabolitePilot™ Software
82-85
Removing Bottlenecks in Metabolite ID Data Analysis with
MetabolitePilot™ Software
For Research Use Only. Not for use in diagnostic procedures.
Xenobiotic Metabolism Workflows in
Drug Discovery and Development
Introduction
6-15
Contents
Xenobiotic Metabolism Workflows in Drug Discovery and Development
RUO-MKT-01-1583-A
Technology Spotlight
16-27
Technology Drives High Performance in Biomolecular Mass Spectrometry
16-19
High Resolution Time-of-Flight MS
20-23
PCVG: A Powerful Algorithm for Automating Comprehensive Xenobiotic
Metabolite Identification
24-27
Early Discovery
28-43
DiscoveryQuant™ Software 2.0: The Definitive Solution for LC/MS/MS
Early-ADME Workflows
28-31
Confirmation of In Vitro Nefazodone Metabolites Using the Superior
Fragmentation of the QTRAP® 5500 LC/MS/MS System
32-37
Breakthrough Productivity for ADME Studies Using the
AB SCIEX TripleTOF® 5600 System
38-43
44-67
Late Stage Discovery
44-47
Rapid Metabolite Identification Using MetabolitePilot™ Software
and the TripleTOF® 5600 System
48-51
In Vivo Metabolic Profiling of Carbamazepine Using the QTRAP® 5500 System
and LightSight® Software 2.2
52-57
Comprehensive Detection of Metabolites Using Polarity Switching Data
Collection with the QTRAP® 5500 LC/MS/MS System
58-61
Simultaneous Pharmacokinetic Profiling and Automated Metabolite
Identification Using the AB SCIEX TripleTOF™ 5600 System
and MetabolitePilot™ Software
62-67
Metabolite Identification with the QTRAP® 5500 LC/MS/MS System:
Sensitivity, Selectivity, Speed, and Unique Workflows
68-85
Definitive Metabolite ID
68-71
Simultaneous Metabolite Identification and Quantitation with
UV Data Integration Using LightSight® Software 2.2
72-77
Differential Mobility Spectrometry for Quantitative and Qualitative Applications
in Pharmaceutical Workflows
78-81
Solving Bottlenecks in Metabolite Identification Using TripleTOF® Systems
and MetabolitePilot™ Software
82-85
Removing Bottlenecks in Metabolite ID Data Analysis with
MetabolitePilot™ Software
Xenobiotic Metabolism Workflows in
Drug Discovery and Development
precursor ions are used to filter unbiased scan data to arrive at
relevant peaks related to parent drug material.4-6 (A complete lists
of filters types are listed or reviewed in the given references.2,3)
Emerging to further expand the tool box for metabolite
identification are strategies for real-time filtering of non-targeted
data during acquisition that can keep pace with the shorter
time scales during ultra-high pressure liquid chromatography
(UHPLC) separations and identify drug-related material as it is
being acquired.2,3
Laura Baker 1, Suma Ramagiri 2
1
Contract Technical Writer at AB SCIEX, Pittsburgh, PA, 2AB SCIEX, Concord, Canada
To stay competitive in the field of modern therapeutics, the
pharmaceutical industry must deliver increasingly potent and
targeted new medicines at an ever faster pace. The discovery
and development process for new drugs is a high-risk venture,
where numerous prospective candidates are screened, but only
one in five compounds in the development stage will successfully
complete clinical research studies to become a marketable drug
(Figure 1). Key to this process is assessing a compound’s suitability
as a drug, and defining its metabolic profile is essential to ensure
that its downstream metabolites are not toxic. Rapid feedback on
metabolite composition during early stages of drug development
means a more effective screening process, preventing the costly
possibility of an unsuitable candidate progressing too far down
the development pipeline.
Characterization of the body’s metabolic response to a drug has
evolved from a late-stage regulatory requirement to an essential
and pivotal finding in the discovery stage that must be attained
quickly alongside in vitro biological screening results. This shift
towards earlier metabolite profiling during the drug discovery
process has impacted pharmaceutical discovery workflows,
requiring evaluation of drug metabolism properties at every step
(Figure 2). In the early discovery stage, metabolic stability and
its influence on pharmacological response is explored at a more
cursory level relying on in vitro analysis to help steer the selection
of lead candidates. A drug must have appropriate bioavailability
and efficacy over a desired interval of time to be successful,
leading to increased efforts to characterize metabolites early in
the discovery stage and to provide critical soft spot analysis for
lead optimization.
Later stage metabolic profiling may reveal that in vitro metabolites
are different from those obtained in vivo; furthermore, in vivo
metabolites may vary between animal models.1 Comparison of
metabolites from these screens early in the process allows for the
selection of appropriate animal models for toxicology screens.2 In
the development stage, application for new drug status with the
FDA mandates that a high level of metabolic characterization be
delivered, including in-depth structural elucidation and metabolite
identification.4 Metabolites may clear from the body differently
from the parent drug and act by differing enzymatic mechanisms;
thus, it is ideal to have detailed pharmacokinetic and absolute
quantitative information in hand before clinical studies begin.
Appreciating that the safety of patients is compromised if the
toxicological effects of a new drug are not well understood, a
drug candidate’s metabolic fingerprint can constructively shape
the design of a clinical trial.2 The substantial impact that metabolic
parameters have on a drug’s continued success through the
development pipeline and the effectiveness of providing
in-depth metabolic information earlier in this process highlights
the increasing need for high-throughput bioanalytical techniques
that rapidly deliver comprehensive metabolic profiles.
In the past decade, metabolite identification workflows have
evolved fundamentally due to breakthrough advances in mass
spectrometry technologies.4 This shift has driven the movement
from analytical compound-based mass spectrometry workflows,
which were often multi-step and multi-injection, towards more
generic workflows that focus on extracting metabolite structural
information using post-acquisition processing of unbiased,
non-targeted mass spectral data (all-in-one approach).4 Traditional
Figure 2: Metabolite profiling tasks required for each stage of the drug discovery
process.
phase I and II metabolite detection often involves two separate
injections, including a full scan of the sample and then a second
scan to perform MS/MS of pre-selected peaks that correspond to
potential parent drug metabolites.2,4 This dual injection approach
is very laborious and further consumes valuable sample; however,
the primary limiting factor is the processing of the extensive
data files generated during non-targeted fragmentation of every
component included within the sample.3 MS/MS fragment ion
interpretation and assignment of new metabolite structures is
very labor-intensive and typically conducted using non-integrated,
independent software packages, adding complexity to an already
daunting process. Increasing to the challenge, drug studies are
frequently carried out in biological fluids or tissues, so low-level
metabolites from clinically-relevant doses of the parent drug
must be detected against a backdrop of competing ions that can
suppress signals of interest.3 For the pharmaceutical scientist, the
key to overcoming this data bottleneck at the discovery stage
is to rapidly find, identify, and confirm low-level metabolites in
complex matrices using highly sensitive and selective automated
processes – and obtain this data from just a single injection.
This resource on xenobiotic metabolism workflows explores fast,
efficient mass spectrometry techniques that meet the challenge
of high-throughput metabolite identification and quantitation
during the drug discovery and development process. With the
advent of a new generation of high resolution mass spectrometers
(HRMS), including Fourier transform mass spectrometry and
hybrid triple quadrupole/time-of-flight instruments, non-targeted,
unbiased detection of all metabolites, even unpredicted, can
be carried out in just one run.2,4 What has emerged as the new
standard for metabolite studies is the coupling of high resolution,
accurate mass instruments with effective computational strategies
for filtering the large data sets from all-in-one fragmentation
approaches, easing interpretation and increasing throughput.2-5
Newer workflows emphasize post-acquisition analysis of data files,
where metabolite predictions based on known biotransformation
activities, intensity cut-offs, neutral loss, mass defects, and
INTRODUCTION
INTRODUCTION
For Research Use Only. Not for use in diagnostic procedures.
Herein we present an extensive resource composed of drug
metabolite identification and quantitation studies that were
performed by our own scientists, customers, and collaborators,
which reveal how highly selective and sensitive detection of lowlevel metabolites can be carried out on state-of-the-art AB SCIEX
mass spectrometers, even in the presence of the high background
noise from biological samples.
•
•
•
Section 1: We explore high-throughput ADMET (adsorption,
distribution, metabolism, excretion and toxicity) screening workflows
that are employed in early drug discovery. Efficiency is essential due to
the large number of compounds to be analyzed, and we focus on the
automation of screening methods using DiscoveryQuant™ Software,
as well as sensitive metabolite profiling using a predictive multiple
reaction monitoring (pMRM) approach on a rapidly scanning hybrid
triple quadrupole/linear ion trap mass spectrometer, the QTRAP® 6500
System. Non-selective data acquisition analyzed using a real-time
multiple mass defect filter (RT-MMDF) is employed on a TripleTOF® 5600
System for high resolution, accurate mass detection and bioanalysis of
pharmacokinetic samples.
Section 2: Late stage discovery workflows are examined, evaluating
technologies that integrate quantitative and qualitative processing
in one run at high speeds and high sensitivity. Automated, rapid
metabolite identification conducted on the TripleTOF 5600 system
with MetabolitePilot™ Software enables high-throughput structure
analysis and quantitation. Metabolite data captured using non-targeted
SWATH™ Acquisition uncovers unpredicted, low-level metabolites.
Workflows on the QTRAP 6500 system with LightSight® Software are
dedicated finding very low-level phase I metabolites and reactive phase
II metabolite conjugates.
Section 3: Definitive metabolite identification during DMPK (drug
metabolism and pharmacokinetics) workflows is highlighted,
examining how mass accuracy and mass-defect-triggered, informationdependent acquisition (IDA) enable assisted structural characterization
of even minor metabolites in complex matrices with the TripleTOF
5600 system and MetabolitePilot Software. Metabolite signal and
detection is augmented after removing competing interferences using
the orthogonal filtering capacity of SelexION™ Differential Mobility
Separation (DMS) Technology for improved selectivity in complex,
in vivo samples. LightSight Software integrates external data sources
(such as UV quantitation) with mass spectrometry results for improved
estimation of metabolite concentration.
Figure 1: Drug discovery and development pipeline.
6
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
7
Xenobiotic Metabolism Workflows in
Drug Discovery and Development
precursor ions are used to filter unbiased scan data to arrive at
relevant peaks related to parent drug material.4-6 (A complete lists
of filters types are listed or reviewed in the given references.2,3)
Emerging to further expand the tool box for metabolite
identification are strategies for real-time filtering of non-targeted
data during acquisition that can keep pace with the shorter
time scales during ultra-high pressure liquid chromatography
(UHPLC) separations and identify drug-related material as it is
being acquired.2,3
Laura Baker 1, Suma Ramagiri 2
1
Contract Technical Writer at AB SCIEX, Pittsburgh, PA, 2AB SCIEX, Concord, Canada
To stay competitive in the field of modern therapeutics, the
pharmaceutical industry must deliver increasingly potent and
targeted new medicines at an ever faster pace. The discovery
and development process for new drugs is a high-risk venture,
where numerous prospective candidates are screened, but only
one in five compounds in the development stage will successfully
complete clinical research studies to become a marketable drug
(Figure 1). Key to this process is assessing a compound’s suitability
as a drug, and defining its metabolic profile is essential to ensure
that its downstream metabolites are not toxic. Rapid feedback on
metabolite composition during early stages of drug development
means a more effective screening process, preventing the costly
possibility of an unsuitable candidate progressing too far down
the development pipeline.
Characterization of the body’s metabolic response to a drug has
evolved from a late-stage regulatory requirement to an essential
and pivotal finding in the discovery stage that must be attained
quickly alongside in vitro biological screening results. This shift
towards earlier metabolite profiling during the drug discovery
process has impacted pharmaceutical discovery workflows,
requiring evaluation of drug metabolism properties at every step
(Figure 2). In the early discovery stage, metabolic stability and
its influence on pharmacological response is explored at a more
cursory level relying on in vitro analysis to help steer the selection
of lead candidates. A drug must have appropriate bioavailability
and efficacy over a desired interval of time to be successful,
leading to increased efforts to characterize metabolites early in
the discovery stage and to provide critical soft spot analysis for
lead optimization.
Later stage metabolic profiling may reveal that in vitro metabolites
are different from those obtained in vivo; furthermore, in vivo
metabolites may vary between animal models.1 Comparison of
metabolites from these screens early in the process allows for the
selection of appropriate animal models for toxicology screens.2 In
the development stage, application for new drug status with the
FDA mandates that a high level of metabolic characterization be
delivered, including in-depth structural elucidation and metabolite
identification.4 Metabolites may clear from the body differently
from the parent drug and act by differing enzymatic mechanisms;
thus, it is ideal to have detailed pharmacokinetic and absolute
quantitative information in hand before clinical studies begin.
Appreciating that the safety of patients is compromised if the
toxicological effects of a new drug are not well understood, a
drug candidate’s metabolic fingerprint can constructively shape
the design of a clinical trial.2 The substantial impact that metabolic
parameters have on a drug’s continued success through the
development pipeline and the effectiveness of providing
in-depth metabolic information earlier in this process highlights
the increasing need for high-throughput bioanalytical techniques
that rapidly deliver comprehensive metabolic profiles.
In the past decade, metabolite identification workflows have
evolved fundamentally due to breakthrough advances in mass
spectrometry technologies.4 This shift has driven the movement
from analytical compound-based mass spectrometry workflows,
which were often multi-step and multi-injection, towards more
generic workflows that focus on extracting metabolite structural
information using post-acquisition processing of unbiased,
non-targeted mass spectral data (all-in-one approach).4 Traditional
Figure 2: Metabolite profiling tasks required for each stage of the drug discovery
process.
phase I and II metabolite detection often involves two separate
injections, including a full scan of the sample and then a second
scan to perform MS/MS of pre-selected peaks that correspond to
potential parent drug metabolites.2,4 This dual injection approach
is very laborious and further consumes valuable sample; however,
the primary limiting factor is the processing of the extensive
data files generated during non-targeted fragmentation of every
component included within the sample.3 MS/MS fragment ion
interpretation and assignment of new metabolite structures is
very labor-intensive and typically conducted using non-integrated,
independent software packages, adding complexity to an already
daunting process. Increasing to the challenge, drug studies are
frequently carried out in biological fluids or tissues, so low-level
metabolites from clinically-relevant doses of the parent drug
must be detected against a backdrop of competing ions that can
suppress signals of interest.3 For the pharmaceutical scientist, the
key to overcoming this data bottleneck at the discovery stage
is to rapidly find, identify, and confirm low-level metabolites in
complex matrices using highly sensitive and selective automated
processes – and obtain this data from just a single injection.
This resource on xenobiotic metabolism workflows explores fast,
efficient mass spectrometry techniques that meet the challenge
of high-throughput metabolite identification and quantitation
during the drug discovery and development process. With the
advent of a new generation of high resolution mass spectrometers
(HRMS), including Fourier transform mass spectrometry and
hybrid triple quadrupole/time-of-flight instruments, non-targeted,
unbiased detection of all metabolites, even unpredicted, can
be carried out in just one run.2,4 What has emerged as the new
standard for metabolite studies is the coupling of high resolution,
accurate mass instruments with effective computational strategies
for filtering the large data sets from all-in-one fragmentation
approaches, easing interpretation and increasing throughput.2-5
Newer workflows emphasize post-acquisition analysis of data files,
where metabolite predictions based on known biotransformation
activities, intensity cut-offs, neutral loss, mass defects, and
INTRODUCTION
INTRODUCTION
For Research Use Only. Not for use in diagnostic procedures.
Herein we present an extensive resource composed of drug
metabolite identification and quantitation studies that were
performed by our own scientists, customers, and collaborators,
which reveal how highly selective and sensitive detection of lowlevel metabolites can be carried out on state-of-the-art AB SCIEX
mass spectrometers, even in the presence of the high background
noise from biological samples.
•
•
•
Section 1: We explore high-throughput ADMET (adsorption,
distribution, metabolism, excretion and toxicity) screening workflows
that are employed in early drug discovery. Efficiency is essential due to
the large number of compounds to be analyzed, and we focus on the
automation of screening methods using DiscoveryQuant™ Software,
as well as sensitive metabolite profiling using a predictive multiple
reaction monitoring (pMRM) approach on a rapidly scanning hybrid
triple quadrupole/linear ion trap mass spectrometer, the QTRAP® 6500
System. Non-selective data acquisition analyzed using a real-time
multiple mass defect filter (RT-MMDF) is employed on a TripleTOF® 5600
System for high resolution, accurate mass detection and bioanalysis of
pharmacokinetic samples.
Section 2: Late stage discovery workflows are examined, evaluating
technologies that integrate quantitative and qualitative processing
in one run at high speeds and high sensitivity. Automated, rapid
metabolite identification conducted on the TripleTOF 5600 system
with MetabolitePilot™ Software enables high-throughput structure
analysis and quantitation. Metabolite data captured using non-targeted
SWATH™ Acquisition uncovers unpredicted, low-level metabolites.
Workflows on the QTRAP 6500 system with LightSight® Software are
dedicated finding very low-level phase I metabolites and reactive phase
II metabolite conjugates.
Section 3: Definitive metabolite identification during DMPK (drug
metabolism and pharmacokinetics) workflows is highlighted,
examining how mass accuracy and mass-defect-triggered, informationdependent acquisition (IDA) enable assisted structural characterization
of even minor metabolites in complex matrices with the TripleTOF
5600 system and MetabolitePilot Software. Metabolite signal and
detection is augmented after removing competing interferences using
the orthogonal filtering capacity of SelexION™ Differential Mobility
Separation (DMS) Technology for improved selectivity in complex,
in vivo samples. LightSight Software integrates external data sources
(such as UV quantitation) with mass spectrometry results for improved
estimation of metabolite concentration.
Figure 1: Drug discovery and development pipeline.
6
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
7
Title
“Software automation tools for increased
throughput metabolic soft-spot identification
in early drug discovery”
Article Highlights
•
•
•
“Comparison of information-dependent acquisition,
SWATH™ Acquisition, and MSAll Techniques in
•
metabolite identification study employing
ultrahigh-performance liquid chromatography—
quadrupole time-of-flight mass spectrometry”
•
•
•
“Identification of urinary metabolites of imperatorin
with a single run on an LC/Triple TOF system based
on multiple mass defect filter data acquisition and
multiple data mining techniques”
•
•
•
•
“Identification of metabolites of deoxyschizandrin
in rats by UPLC-Q-TOF-MS/MS based on multiple
mass defect filter data acquisition and multiple data
processing techniques”
•
•
•
•
“Standardized workflows for increasing efficiency
and productivity in discovery stage bioanalysis”
•
•
•
Citation
Application of software automation tools for rapidly identifying metabolites using mass defect
filtering and HRMS instruments
Review of MetabolitePilot™ Software and Mass-MetaSite™ Software workflows for finding peaks,
identifying metabolites, and elucidating structures
Focus was on accuracy and throughput for the localization of the primary soft spots on firstgeneration metabolites of proprietary compounds
Zelesky V, Schneider, R,
Janiszewski J, Zamora I, Ferguson
J, Troutman M. Bioanalysis. 2013;
5(10): 1165-1179.
Evaluation of methods for acquiring MS/MS data using an HRMS instrument for metabolite ID in
microsomes and urine
Comparison of IDA, SWATH™ Acquisition, and MSAll data acquisition methodologies using eight
non-proprietary compounds that produced a diverse array of metabolites
SWATH Acquisition and MSAll methods surpassed IDA hit rates, triggering MS/MS for all metabolites,
while IDA methods produced superior MS/MS data for facilitation of structural assignment.
MS/MS spectra quality is highly dependent on the Q1 selection window width (and thus the
acquisition method).
Zhu Z, Chen Y, Subramanian
R. Analytical Chemistry. 2014;
86(2):1202-9.
A generic, single-injection approach for detecting in vivo metabolites, including low-level, of an
herbal Chinese medicine using HRMS instrumentation and a single injection protocol
Data acquisition was conducted using real-time multiple mass defect filters (MMDF) combined
with dynamic background subtraction to identify urinary metabolites.
Low-level metabolites were identified from high background and excess endogenous components
by combining HRMS data mining techniques (XIC, MDF, PIF, and NLF).
Structures for 44 phase I and 7 phase II metabolites were reported using this powerful,
integrated approach.
Qiao S, Shi X, Shi R, Liu M, Liu T,
Zhang K, Wang Q, Yao M, Zhang
L. Anal Bioanal Chem. 2013; 405:
6721-6738.
Development of a novel and efficient strategy for screening the in vivo metabolites of an herbal
Chinese medicine using HRMS instrumentation and a single injection protocol
Filters such as MMDF and DBS were applied on-line during data acquisition, and multiple post
data-acquisition filters (XIC, MDF, PIF, and NLF) were combined to obtain 51 phase I and II metabolites
in rat urine and bile.
MetabolitePilot Software identified metabolites, and structure elucidation was enabled by accurate
mass information, biotransformation knowledge, and fragmentation patterns.
A unique, Clog P value was assigned to compounds to distinguish between multiple isomers, which
was based on varying retention times for isomers.
Liu M, Zhao S, Wang Z, Wang Y,
Liu T, Song L, Wang C, Wang H,
Tu P. Journal of Chromatography
B. 2014; 949-950:115-126.
Discussion of standardized discovery LC-MS workflows for high-throughput, small-molecule
bioanalysis and the efficiency gains after implementation
Bioanalytical processes (compound tuning, LC method development, analytical acceptance criteria,
automated sample preparation, sample analysis platforms, data processing, and data reporting) were
harmonized across multiple research sites.
Reducing time and resources on routine bioanalysis has allowed for more challenging studies and
development of future research.
Bateman KP, Cohen L, Emary B,
Pucci V. Bioanalysis. 2013; 5(14):
1783-1794.
“Bioactivation of sitaxentan in liver microsomes,
hepatocytes, and expressed human P450s with
characterization of the glutathione conjugate by
liquid chromatography tandem mass spectrometry”
• Identification of a reactive metabolite and its structure for an endothelin-A receptor antagonist
withdrawn for idiosyncratic drug toxicity
• Characterization of an in vitro GSH-conjugate in liver microsomes from wide array of mammals using
hybrid triple quadrupole/time-of-flight mass spectrometry full scan data and product ion spectra
• Reactive metabolite inhibition of a specific P450 isoform was demonstrated through competitive and
time-dependent assays and proposed as a mechanism for drug toxicity.
Erve JCL, Gauby S, Maynard,
Jr. JW, Svensson MA, Tonn G,
Quinn KP. Chemical Research in
Toxicology. 2013; 26: 926-936.
“Drug metabolite profiling and identification by
high-resolution mass spectrometry”
• Overview of HRMS acquisition methods (both targeted and non-targeted) and data mining techniques
(mass defect, product ion, isotope pattern filters, and background subtraction)
• Review of single HRMS platforms with the capacity for multiple metabolite ID tasks
• Future developments for metabolite ID on HRMS instruments
Zhu, M, Zhang, H, Humphreys
WG. J Biol Chem. 2011; 286 (29):
25419-25425.
*These articles were reprinted with permission in the first 30 copies of this resource.
Each section and experiment featured in this resource includes
an overview of the key challenges, benefits, and features of
the bioanalytical technique presented. In this way, the mass
spectrometric techniques and metabolic data analysis can be put
into context with other bioanalytical tools and help highlight
the many advantages that LC/MS/MS offers to all stages of
drug discovery and development. For further reference, Table 1
showcases the current literature, reviewing the trends and novel
techniques that are behind the high-throughput innovations for
detecting and identifying metabolites. Presented below are some
of the key challenges and benefits of metabolite identification,
alongside the innovative analytical instruments that AB SCIEX
features for accelerating workflows, meeting the demands for
speed, efficiency, and depth of data that are vital to intelligent
and effective drug optimization.
Key challenges of metabolite identification, structural
assignment, and bioanalysis
Analyzing very small amounts of drug-related material obscured
by cellular components presents a unique challenge to a process
that requires increasing levels of productivity to attain the high
throughput necessary for the efficient screening of thousands
of compounds (Table 2). The interfering signals from the
complex matrices that harbor drug metabolites can prolong data
processing times or impede the collection of adequate MS/MS
information. Structural assignment is often a painstaking, manual
process, relying on a biotransformation scientist’s expertise to
unravel the data. Capturing information on all metabolites,
particularly low-level, in the early stages, is of particular
importance to the selection of viable drug candidates. Missing
a potentially toxic compound during the screening process due
to incomplete data collection may cause valuable resources to
be directed towards an unsuitable candidate that may later
fail during clinical trials. (An example of a drug with a boxed
warning, pulled from the market due to a previously-undetectable
reactive metabolite, is given.7) Testing is often completed at low,
therapeutically-relevant concentrations, which hinders accurate
concentration determination and requires the use of highly
sensitive instrumentation. In many cases, data is obtained using
multiple, non-integrated platforms making it difficult to transfer
data to different applications but also making it problematic to
share data with other groups. Limitations on sample availability
strongly impact workflow design, and even a single re-injection
to re-evaluate a peak or a missed compound requires expensive
and time-consuming re-experimenation. To tackle these collective
challenges, advanced LC/MS/MS techniques have been developed
that are responsive to industry standards for productivity levels
requisite for the successful screening of viable compounds.
INTRODUCTION
INTRODUCTION
For Research Use Only. Not for use in diagnostic procedures.
Key benefits of LC/MS/MS workflows for metabolite
profiling
AB SCIEX is dedicated to overcoming drug discovery bottlenecks
and automating the metabolic profiling process by developing
high-throughput, intelligent workflows that fuel drug discovery.
Improvements to instrument capacity, data processing,
and software design have propelled metabolite workflows
forward, attaining the efficiency and productivity essential for
accommodating large sample batches and the screening of
thousands of compounds. (See Table 3 for an overview of the
benefits of specific AB SCIEX technologies.) Crucial to quicker
sample run times are fast scanning speeds and the mass
spectrometer’s capacity to collect data in time frames compatible
with UHPLC sample elution. Data collection on orbital trapping
instruments is hampered by the slower scanning speeds needed
to achieve high resolution, but both the TripleTOF 5600 system
and the QTRAP 6500 system have the speed and power for
rapid MS and MS/MS analysis. The TripleTOF 5600 system
maintains approximately 30K resolution regardless of the analysis
speed and provides accurate mass data for the unambiguous
assignment of elemental composition, as well as the sensitivity
and linearity of a triple quadruple instrument for excellent
quantitative performance. Prior to the development of hybrid
machines, separate MS instruments were employed for accurate
quantitation and qualitative discovery of metabolites. The high
sensitivity of the QTRAP 6500 system permits metabolic profiling
at physiologically relevant dosing and enables the discovery of
Table 1: Selected citations for further reading on metabolism workflows in drug discovery and development
High Res XICs
counts
SWATH™ Acquisition
25 Da
CID
retention time
TOF
counts
Q1
m/z
Figure 3: A representation of SWATH™ Acquisition on the TripleTOF ® 5600 System.
8
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
9
Title
“Software automation tools for increased
throughput metabolic soft-spot identification
in early drug discovery”
Article Highlights
•
•
•
“Comparison of information-dependent acquisition,
SWATH™ Acquisition, and MS All Techniques in
•
metabolite identification study employing
ultrahigh-performance liquid chromatography—
quadrupole time-of-flight mass spectrometry”
•
•
•
“Identification of urinary metabolites of imperatorin
with a single run on an LC/Triple TOF system based
on multiple mass defect filter data acquisition and
multiple data mining techniques”
•
•
•
•
“Identification of metabolites of deoxyschizandrin
in rats by UPLC-Q-TOF-MS/MS based on multiple
mass defect filter data acquisition and multiple data
processing techniques”
•
•
•
•
“Standardized workflows for increasing efficiency
and productivity in discovery stage bioanalysis”
•
•
•
Citation
Application of software automation tools for rapidly identifying metabolites using mass defect
filtering and HRMS instruments
Review of MetabolitePilot™ Software and Mass-MetaSite™ Software workflows for finding peaks,
identifying metabolites, and elucidating structures
Focus was on accuracy and throughput for the localization of the primary soft spots on firstgeneration metabolites of proprietary compounds
Zelesky V, Schneider, R,
Janiszewski J, Zamora I, Ferguson
J, Troutman M. Bioanalysis. 2013;
5(10): 1165-1179.
Evaluation of methods for acquiring MS/MS data using an HRMS instrument for metabolite ID in
microsomes and urine
Comparison of IDA, SWATH™ Acquisition, and MSAll data acquisition methodologies using eight
non-proprietary compounds that produced a diverse array of metabolites
SWATH Acquisition and MSAll methods surpassed IDA hit rates, triggering MS/MS for all metabolites,
while IDA methods produced superior MS/MS data for facilitation of structural assignment.
MS/MS spectra quality is highly dependent on the Q1 selection window width (and thus the
acquisition method).
Zhu Z, Chen Y, Subramanian
R. Analytical Chemistry. 2014;
86(2):1202-9.
A generic, single-injection approach for detecting in vivo metabolites, including low-level, of an
herbal Chinese medicine using HRMS instrumentation and a single injection protocol
Data acquisition was conducted using real-time multiple mass defect filters (MMDF) combined
with dynamic background subtraction to identify urinary metabolites.
Low-level metabolites were identified from high background and excess endogenous components
by combining HRMS data mining techniques (XIC, MDF, PIF, and NLF).
Structures for 44 phase I and 7 phase II metabolites were reported using this powerful,
integrated approach.
Qiao S, Shi X, Shi R, Liu M, Liu T,
Zhang K, Wang Q, Yao M, Zhang
L. Anal Bioanal Chem . 2013; 405:
6721-6738.
Development of a novel and efficient strategy for screening the in vivo metabolites of an herbal
Chinese medicine using HRMS instrumentation and a single injection protocol
Filters such as MMDF and DBS were applied on-line during data acquisition, and multiple post
data-acquisition filters (XIC, MDF, PIF, and NLF) were combined to obtain 51 phase I and II metabolites
in rat urine and bile.
MetabolitePilot Software identified metabolites, and structure elucidation was enabled by accurate
mass information, biotransformation knowledge, and fragmentation patterns.
A unique, Clog P value was assigned to compounds to distinguish between multiple isomers, which
was based on varying retention times for isomers.
Liu M, Zhao S, Wang Z, Wang Y,
Liu T, Song L, Wang C, Wang H,
Tu P. Journal of Chromatography
B. 2014; 949-950:115-126.
Discussion of standardized discovery LC-MS workflows for high-throughput, small-molecule
bioanalysis and the efficiency gains after implementation
Bioanalytical processes (compound tuning, LC method development, analytical acceptance criteria,
automated sample preparation, sample analysis platforms, data processing, and data reporting) were
harmonized across multiple research sites.
Reducing time and resources on routine bioanalysis has allowed for more challenging studies and
development of future research.
Bateman KP, Cohen L, Emary B,
Pucci V. Bioanalysis. 2013; 5(14):
1783-1794.
“Bioactivation of sitaxentan in liver microsomes,
hepatocytes, and expressed human P450s with
characterization of the glutathione conjugate by
liquid chromatography tandem mass spectrometry”
•
Identification of a reactive metabolite and its structure for an endothelin-A receptor antagonist
withdrawn for idiosyncratic drug toxicity
• Characterization of an in vitro GSH-conjugate in liver microsomes from wide array of mammals using
hybrid triple quadrupole/time-of-flight mass spectrometry full scan data and product ion spectra
• Reactive metabolite inhibition of a specific P450 isoform was demonstrated through competitive and
time-dependent assays and proposed as a mechanism for drug toxicity.
Erve JCL, Gauby S, Maynard,
Jr. JW, Svensson MA, Tonn G,
Quinn KP. Chemical Research in
Toxicology. 2013; 26: 926-936.
“Drug metabolite profiling and identification by
high-resolution mass spectrometry”
•
Zhu, M, Zhang, H, Humphreys
WG. J Biol Chem . 2011; 286 (29):
25419-25425.
•
•
Overview of HRMS acquisition methods (both targeted and non-targeted) and data mining techniques
(mass defect, product ion, isotope pattern filters, and background subtraction)
Review of single HRMS platforms with the capacity for multiple metabolite ID tasks
Future developments for metabolite ID on HRMS instruments
*These articles were reprinted with permission in the first 30 copies of this resource.
Each section and experiment featured in this resource includes
an overview of the key challenges, benefits, and features of
the bioanalytical technique presented. In this way, the mass
spectrometric techniques and metabolic data analysis can be put
into context with other bioanalytical tools and help highlight
the many advantages that LC/MS/MS offers to all stages of
drug discovery and development. For further reference, Table 1
showcases the current literature, reviewing the trends and novel
techniques that are behind the high-throughput innovations for
detecting and identifying metabolites. Presented below are some
of the key challenges and benefits of metabolite identification,
alongside the innovative analytical instruments that AB SCIEX
features for accelerating workflows, meeting the demands for
speed, efficiency, and depth of data that are vital to intelligent
and effective drug optimization.
Key challenges of metabolite identification, structural
assignment, and bioanalysis
Analyzing very small amounts of drug-related material obscured
by cellular components presents a unique challenge to a process
that requires increasing levels of productivity to attain the high
throughput necessary for the efficient screening of thousands
of compounds (Table 2). The interfering signals from the
complex matrices that harbor drug metabolites can prolong data
processing times or impede the collection of adequate MS/MS
information. Structural assignment is often a painstaking, manual
process, relying on a biotransformation scientist’s expertise to
unravel the data. Capturing information on all metabolites,
particularly low-level, in the early stages, is of particular
importance to the selection of viable drug candidates. Missing
a potentially toxic compound during the screening process due
to incomplete data collection may cause valuable resources to
be directed towards an unsuitable candidate that may later
fail during clinical trials. (An example of a drug with a boxed
warning, pulled from the market due to a previously-undetectable
reactive metabolite, is given.7) Testing is often completed at low,
therapeutically-relevant concentrations, which hinders accurate
concentration determination and requires the use of highly
sensitive instrumentation. In many cases, data is obtained using
multiple, non-integrated platforms making it difficult to transfer
data to different applications but also making it problematic to
share data with other groups. Limitations on sample availability
strongly impact workflow design, and even a single re-injection
to re-evaluate a peak or a missed compound requires expensive
and time-consuming re-experimenation. To tackle these collective
challenges, advanced LC/MS/MS techniques have been developed
that are responsive to industry standards for productivity levels
requisite for the successful screening of viable compounds.
INTRODUCTION
INTRODUCTION
For Research Use Only. Not for use in diagnostic procedures.
Key benefits of LC/MS/MS workflows for metabolite
profiling
AB SCIEX is dedicated to overcoming drug discovery bottlenecks
and automating the metabolic profiling process by developing
high-throughput, intelligent workflows that fuel drug discovery.
Improvements to instrument capacity, data processing,
and software design have propelled metabolite workflows
forward, attaining the efficiency and productivity essential for
accommodating large sample batches and the screening of
thousands of compounds. (See Table 3 for an overview of the
benefits of specific AB SCIEX technologies.) Crucial to quicker
sample run times are fast scanning speeds and the mass
spectrometer’s capacity to collect data in time frames compatible
with UHPLC sample elution. Data collection on orbital trapping
instruments is hampered by the slower scanning speeds needed
to achieve high resolution, but both the TripleTOF 5600 system
and the QTRAP 6500 system have the speed and power for
rapid MS and MS/MS analysis. The TripleTOF 5600 system
maintains approximately 30K resolution regardless of the analysis
speed and provides accurate mass data for the unambiguous
assignment of elemental composition, as well as the sensitivity
and linearity of a triple quadruple instrument for excellent
quantitative performance. Prior to the development of hybrid
machines, separate MS instruments were employed for accurate
quantitation and qualitative discovery of metabolites. The high
sensitivity of the QTRAP 6500 system permits metabolic profiling
at physiologically relevant dosing and enables the discovery of
Table 1: Selected citations for further reading on metabolism workflows in drug discovery and development
High Res XICs
counts
SWATH® Acquisition
25 Da
CID
retention time
TOF
counts
Q1
m/z
Figure 3: A representation of SWATH™ Acquisition on the TripleTOF ® 5600 System.
8
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
9
• Overlooking low-level drug metabolites omitted from MS survey scans conducted on
complex biological matrices such as bile, plasma, and tissue extracts
• Repeat sample injections due to inadequate collection of metabolite information during
initial studies leads to costly re-experimentation and re-analysis.
• Non-definitive metabolite identification due to inadequate acquisition of MS/MS information
on minor or unpredicted metabolites
• Multiple, non-integrated software platforms complicate data processing, slowing metabolite
identification and structure elucidation.
• Co-elution of isomeric or isobaric metabolites is one the major bottlenecks during definitive
metabolite identification process. This may result in long chromatographic run times or even
expensive column chemistry separating them before identification.
Table 2: Summary of key challenges of metabolite identification
hard-to-detect, low-level metabolites from just a single run. The
linear ion trap scan speeds on the QTRAP 6500 system at rates
of 20,000 Da/sec enhance information-dependent acquisition
(IDA) coverage as well, allowing for more MS/MS scans on drugrelated peaks. Positive/negative polarization switching on both
machines eliminates the need for separate runs for detecting
phase II metabolites that, unlike phase I metabolites are often only
detected in negative ion mode, allowing for the consolidation of
run times and further gains in workflow efficiency.
Improvements to DiscoveryQuant™ Software have automated
many aspects of method development and sample processing.
The integrated, easy-to-use software provides automated methodbuilding templates, auto sampler support, and batch processing
for fast optimization of sample acquisition without requiring
extensive operator input for every compound. Multiple aspects
of data collection are consolidated into one software package,
so that method building, data acquisition, and processing can
occur on one platform, streamlining data review and saving
time. Data processing platforms (MetabolitePilot and LightSight
Software) have accelerated metabolic profiling with a more
automated approach to interpreting fragment spectra, speeding
up metabolite identification, structural elucidation, and metabolic
site assignment.6 Correlation of metabolites across multiple
samples is possible with batch modes, providing a way to pinpoint
lot-to-lot anomalies or conduct time course studies. Sharing this
data amongst multiple groups and providing rapid feedback
to medicinal chemists is assisted through software linkages to
laboratory information management software (LIMS) and global
databases, easing data transfer between neighboring laboratories
or far-flung research centers.10
Additional software improvements have enhanced the relevancy
of peaks identified as drug-related material with the development
of novel algorithms that can effectively mine large data sets
in a meaningful way and ease fragment interpretation. Data
sets obtained through all-in-one approaches, such as SWATH
Acquisition (Figure 3), form a comprehensive safety net,
capturing both predicted and unpredicted metabolites, and
allow for retroactive re-evaluation of the data in the event of
an overlooked metabolite. But the enormity of the collection
of all MS/MS data for a particular sample raises concerns on
10
RUO-MKT-01-1583-A
Real-time multiple mass defect (RT-MMDF) algorithm on the TripleTOF® 5600 System
• Increased productivity by simultaneously capturing both qualitative and
quantitative data via:
– Single injection workflows that capture both TOF MS and TOF MS/MS
– Increased MS/MS triggering efficiency on a UPLC time scale (2-3 sec. peak width)
• More accurate identification and confirmation of analytes in complex, in vivo samples
(e.g., plasma with PEGs, bile samples, tissue samples)
SWATH™ Acquisition for data-independent, all-in-one MS/MS fragmentation
• Comprehensive quantitative and qualitative analysis in a single injection captures
MS/MS information for both predicted and unpredicted metabolites, creating the ultimate
safety net
• Informative, more complete MS/MS spectra for better metabolite structure
prediction and site-modification identification including:
– All-inclusive MS/MS for low-level metabolite/catabolite ID
– Retention of isotope pattern in MS/MS for each fragment (14C/SIL ADC
metabolism studies)
– Spectra are less complex than traditional DIA techniques and display more
drug-related peaks.
AB SCIEX TripleTOF® 5600 System
Accurate mass at the speed and sensitivity of a triple quadrupole:
• Fast MS and MS/MS acquisition speeds compatible with fast chromatography
• Resolution over 30,000
• External mass accuracy ~1 ppm
• 4 orders of linear dynamic range
AB SCIEX QTRAP® 5500 System
A single platform for drug metabolism and bioanalytical quantification workflows
• Targeted and non-targeted workflows
• Increased sensitivity
• Increased speed
• Full quantitative capabilities
MetabolitePilot™ Software
Accurate mass data processing and interrogation:
• Generate cleaner, more relevant data with multiple mass defect filtering (MMDF)
• Store and retrieve critical information in the compound library and results database
• Quickly process multiple sample sets in batches
• Increase confidence in your results with intelligent scoring and easy-to-visualize color-coding
• Predict formulae with a high level of chemical intelligence
LightSight® Software
Exploit the full functionality of QTRAP® Technology and processing strategies for multiple
metabolite ID workflows
• pMRM – high-sensitivity, targeted approach for really low-level detection and confirmation
• PI/NL (+/-ve polarity switching) – structure-based filtering approach ideal for reactive metabolite
screening
• Multiple ion monitoring and Q3 single MS strategies for a non-targeted approach
INTRODUCTION
INTRODUCTION
For Research Use Only. Not for use in diagnostic procedures.
• High-resolution quantification reduces the potential for interferences, yet
maintains the sensitivity and dynamic range of leading triple quads.
– Selective, MRM-style quantitation using product ion mass and summation of
product ions
• Easy method development and retrospective data mining
– Requirement for sample-specific method development is eliminated.
– Creation of a digital archive of all analytes enables post-acquisition investigation
without additional injections.
SelexION™ Technology for metabolite identification workflows
• Capable of separating isobaric metabolites or isobaric co-administered drugs
– Reduced chromatographic run times accelerate productivity
– Elimination of tedious LC method development and expensive columns reduce cost
of analysis
– Selective and specific quantification provide accurate results for PK profiling and
clearance rates.
– Enhanced structure elucidation capabilities achieved with more relevant, less complex
MS/MS spectra
• Elimination of background noise and co-eluting interference
– Increased sensitivity lowers the LLOQ and boosts the S/N ratio
– Better peak integration facilitates improvement to data quality benchmarks (%CV,
accuracy, dynamic range, LLOQ)
Table 3: Key benefits of pivotal AB SCIEX metabolite screening workflows
how to isolate drug-related material that is overshadowed by
background noise. SWATH Acquisition employs an algorithm
called principal components variable grouping (PCVG) analysis
to filter unbiased data sets, highlighting peaks of interest while
removing obfuscating background or chemical noise. The
resulting spectra, overall, are less complex after PCVG-filtering
and display more relevant drug-related peaks, which represent an
inclusive record of all metabolites and their changes over time.2,3
In that vein, the algorithm for real-time multiple mass defect
filtering (RT-MMDF) performs a similar feat, but is employed
during data-dependent acquisition. (Examples of RT-MMDF
applied to metabolite discovery are provided.8,9) During UHPLC/
MS acquisition, the RT-MMDF filter identifies peaks related to
the parent drug, selectively triggering MS/MS scans to produce a
trimmer data set that accentuates metabolite information without
further post-acquisition processing (Figure 4). Both of these
innovative approaches – PCVG-filtering and RT-MMDF – continue
to effectively redefine and re-invigorate the solutions needed for
the complex challenges of therapeutic drug development.
DRUG METABOLISM
Table 4: Simple, clear metabolism workflows for every stage of drug discovery. The TripleTOF™ 5600 System and the MetabolitePilot™ Software deliver high sensitivity
quantification and new qualitative capabilities on a single platform. The QTRAP® 5500 and LightSight® Software use predictive MRM and multiple precursor ion and/or neutral
loss survey scans in a single analysis, including polarity switching.
Key features of AB SCIEX instrumentation for metabolite
profiling
As a global front-runner in the development of innovative
technology, AB SCIEX continues to provide technological solutions
that are transforming the drug pipeline. Each step of drug
development has a unique set of benchmarks that must be met
prior to advancement (Figure 2). Early in the discovery phase,
rapid structural identification and in vitro metabolic stability
results are used to screen thousands of compounds and drive the
decision-making process surrounding compound optimization.
Development phase requirements for integrated qual/quant
workflows, quantitation of low-level reactive metabolites, and
high-level structure elucidation must meet the compliance and
validation standards of regulatory agencies and clinical safety.
Mindful of the need for high productivity, AB SCIEX has advanced
high-throughput workflows and remarkable, high-performance
instruments that streamline the journey from the lab to the clinic
for the development of modern, new therapeutics. Discussed
below are the workflows and instruments that have set the
industry’s standards for competitive operation (summarized in
Table 4).
1) High-resolution mass spectrometry quant/qual workflows:
TripleTOF 5600 system and MetabolitePilot Software
The superb quantitative capacity of a triple quadrupole and the
high-performance accurate mass analyzer of a high-resolution
www.absciex.com
www.absciex.com
time-of-flight mass spectrometer are united into one innovative,
hybrid instrument, the TripleTOF 5600 system – that performs
both quantitative and qualitative analyses with just one method.
Throughout every stage of the drug discovery process, it is
essential to find, identify, and confirm metabolites as quickly as
possible, and the workflows designed for the TripleTOF system
enable the fast, accurate metabolic profiling necessary for both
ADMET and DMPK studies. The collection of information-rich
data using a specialized quant/qual workflow (Figure 5) and
the selective extraction of drug-related information during data
acquisition produce highly-relevant structural information for
detection and characterization of metabolites in the same run.
The TripleTOF system has the capacity to collect both MS and
MS/MS scan data simultaneously; whilst survey scans generate
TOF-MS spectra, a real-time filter based on mass defects
(RT-MMDF) triggers MS/MS acquisition of peaks similar to the
parent drug, rather than on unrelated background noise, so
that only highly relevant information is exhibited in the resulting
spectra. Another feature of the hybrid instrumentation is the
combination of high resolution and high detector speed that can
maintain sufficiently high resolution at low m/z, thereby including
even more fragments in the equation for unambiguous structural
assignment. (For a full listing of TripleTOF system features, see
Table 5.) Altogether, intelligent data acquisition, simultaneous
quant/qual analyses, and high scan speeds packaged in one hybrid
instrument can support these powerful metabolite workflows.
DRUG METABOLISM
11
• Overlooking low-level drug metabolites omitted from MS survey scans conducted on
complex biological matrices such as bile, plasma, and tissue extracts
• Repeat sample injections due to inadequate collection of metabolite information during
initial studies leads to costly re-experimentation and re-analysis.
• Non-definitive metabolite identification due to inadequate acquisition of MS/MS information
on minor or unpredicted metabolites
• Multiple, non-integrated software platforms complicate data processing, slowing metabolite
identification and structure elucidation.
• Co-elution of isomeric or isobaric metabolites is one the major bottlenecks during definitive
metabolite identification process. This may result in long chromatographic run times or even
expensive column chemistry separating them before identification.
Table 2: Summary of key challenges of metabolite identification
hard-to-detect, low-level metabolites from just a single run. The
linear ion trap scan speeds on the QTRAP 6500 system at rates
of 20,000 Da/sec enhance information-dependent acquisition
(IDA) coverage as well, allowing for more MS/MS scans on drugrelated peaks. Positive/negative polarization switching on both
machines eliminates the need for separate runs for detecting
phase II metabolites that, unlike phase I metabolites are often only
detected in negative ion mode, allowing for the consolidation of
run times and further gains in workflow efficiency.
Improvements to DiscoveryQuant™ Software have automated
many aspects of method development and sample processing.
The integrated, easy-to-use software provides automated methodbuilding templates, auto sampler support, and batch processing
for fast optimization of sample acquisition without requiring
extensive operator input for every compound. Multiple aspects
of data collection are consolidated into one software package,
so that method building, data acquisition, and processing can
occur on one platform, streamlining data review and saving
time. Data processing platforms (MetabolitePilot and LightSight
Software) have accelerated metabolic profiling with a more
automated approach to interpreting fragment spectra, speeding
up metabolite identification, structural elucidation, and metabolic
site assignment.6 Correlation of metabolites across multiple
samples is possible with batch modes, providing a way to pinpoint
lot-to-lot anomalies or conduct time course studies. Sharing this
data amongst multiple groups and providing rapid feedback
to medicinal chemists is assisted through software linkages to
laboratory information management software (LIMS) and global
databases, easing data transfer between neighboring laboratories
or far-flung research centers.10
Additional software improvements have enhanced the relevancy
of peaks identified as drug-related material with the development
of novel algorithms that can effectively mine large data sets
in a meaningful way and ease fragment interpretation. Data
sets obtained through all-in-one approaches, such as SWATH
Acquisition (Figure 3), form a comprehensive safety net,
capturing both predicted and unpredicted metabolites, and
allow for retroactive re-evaluation of the data in the event of
an overlooked metabolite. But the enormity of the collection
of all MS/MS data for a particular sample raises concerns on
10
RUO-MKT-01-1583-A
Real-time multiple mass defect (RT-MMDF) algorithm on the TripleTOF® 5600 System
• Increased productivity by simultaneously capturing both qualitative and
quantitative data via:
– Single injection workflows that capture both TOF MS and TOF MS/MS
– Increased MS/MS triggering efficiency on a UPLC time scale (2-3 sec. peak width)
• More accurate identification and confirmation of analytes in complex, in vivo samples
(e.g., plasma with PEGs, bile samples, tissue samples)
SWATH™ Acquisition for data-independent, all-in-one MS/MS fragmentation
• Comprehensive quantitative and qualitative analysis in a single injection captures
MS/MS information for both predicted and unpredicted metabolites, creating the ultimate
safety net
• Informative, more complete MS/MS spectra for better metabolite structure
prediction and site-modification identification including:
– All-inclusive MS/MS for low-level metabolite/catabolite ID
– Retention of isotope pattern in MS/MS for each fragment (14C/SIL ADC
metabolism studies)
– Spectra are less complex than traditional DIA techniques and display more
drug-related peaks.
AB SCIEX TripleTOF® 5600 System
Accurate mass at the speed and sensitivity of a triple quadrupole:
• Fast MS and MS/MS acquisition speeds compatible with fast chromatography
• Resolution over 30,000
• External mass accuracy ~1 ppm
• 4 orders of linear dynamic range
AB SCIEX QTRAP® 5500 System
A single platform for drug metabolism and bioanalytical quantification workflows
• Targeted and non-targeted workflows
• Increased sensitivity
• Increased speed
• Full quantitative capabilities
MetabolitePilot™ Software
Accurate mass data processing and interrogation:
• Generate cleaner, more relevant data with multiple mass defect filtering (MMDF)
• Store and retrieve critical information in the compound library and results database
• Quickly process multiple sample sets in batches
• Increase confidence in your results with intelligent scoring and easy-to-visualize color-coding
• Predict formulae with a high level of chemical intelligence
LightSight® Software
Exploit the full functionality of QTRAP® Technology and processing strategies for multiple
metabolite ID workflows
• pMRM – high-sensitivity, targeted approach for really low-level detection and confirmation
• PI/NL (+/-ve polarity switching) – structure-based filtering approach ideal for reactive metabolite
screening
• Multiple ion monitoring and Q3 single MS strategies for a non-targeted approach
INTRODUCTION
INTRODUCTION
For Research Use Only. Not for use in diagnostic procedures.
• High-resolution quantification reduces the potential for interferences, yet
maintains the sensitivity and dynamic range of leading triple quads.
– Selective, MRM-style quantitation using product ion mass and summation of
product ions
• Easy method development and retrospective data mining
– Requirement for sample-specific method development is eliminated.
– Creation of a digital archive of all analytes enables post-acquisition investigation
without additional injections.
SelexION™ Technology for metabolite identification workflows
• Capable of separating isobaric metabolites or isobaric co-administered drugs
– Reduced chromatographic run times accelerate productivity
– Elimination of tedious LC method development and expensive columns reduce cost
of analysis
– Selective and specific quantification provide accurate results for PK profiling and
clearance rates.
– Enhanced structure elucidation capabilities achieved with more relevant, less complex
MS/MS spectra
• Elimination of background noise and co-eluting interference
– Increased sensitivity lowers the LLOQ and boosts the S/N ratio
– Better peak integration facilitates improvement to data quality benchmarks (%CV,
accuracy, dynamic range, LLOQ)
Table 3: Key benefits of pivotal AB SCIEX metabolite screening workflows
how to isolate drug-related material that is overshadowed by
background noise. SWATH Acquisition employs an algorithm
called principal components variable grouping (PCVG) analysis
to filter unbiased data sets, highlighting peaks of interest while
removing obfuscating background or chemical noise. The
resulting spectra, overall, are less complex after PCVG-filtering
and display more relevant drug-related peaks, which represent an
inclusive record of all metabolites and their changes over time.2,3
In that vein, the algorithm for real-time multiple mass defect
filtering (RT-MMDF) performs a similar feat, but is employed
during data-dependent acquisition. (Examples of RT-MMDF
applied to metabolite discovery are provided.8,9) During UHPLC/
MS acquisition, the RT-MMDF filter identifies peaks related to
the parent drug, selectively triggering MS/MS scans to produce a
trimmer data set that accentuates metabolite information without
further post-acquisition processing (Figure 4). Both of these
innovative approaches – PCVG-filtering and RT-MMDF – continue
to effectively redefine and re-invigorate the solutions needed for
the complex challenges of therapeutic drug development.
DRUG METABOLISM
Table 4: Simple, clear metabolism workflows for every stage of drug discovery. The TripleTOF™ 5600 System and the MetabolitePilot™ Software deliver high sensitivity
quantification and new qualitative capabilities on a single platform. The QTRAP® 5500 and LightSight® Software use predictive MRM and multiple precursor ion and/or neutral
loss survey scans in a single analysis, including polarity switching.
Key features of AB SCIEX instrumentation for metabolite
profiling
As a global front-runner in the development of innovative
technology, AB SCIEX continues to provide technological solutions
that are transforming the drug pipeline. Each step of drug
development has a unique set of benchmarks that must be met
prior to advancement (Figure 2). Early in the discovery phase,
rapid structural identification and in vitro metabolic stability
results are used to screen thousands of compounds and drive the
decision-making process surrounding compound optimization.
Development phase requirements for integrated qual/quant
workflows, quantitation of low-level reactive metabolites, and
high-level structure elucidation must meet the compliance and
validation standards of regulatory agencies and clinical safety.
Mindful of the need for high productivity, AB SCIEX has advanced
high-throughput workflows and remarkable, high-performance
instruments that streamline the journey from the lab to the clinic
for the development of modern, new therapeutics. Discussed
below are the workflows and instruments that have set the
industry’s standards for competitive operation (summarized in
Table 4).
1) High-resolution mass spectrometry quant/qual workflows:
TripleTOF 5600 system and MetabolitePilot Software
The superb quantitative capacity of a triple quadrupole and the
high-performance accurate mass analyzer of a high-resolution
www.absciex.com
www.absciex.com
time-of-flight mass spectrometer are united into one innovative,
hybrid instrument, the TripleTOF 5600 system – that performs
both quantitative and qualitative analyses with just one method.
Throughout every stage of the drug discovery process, it is
essential to find, identify, and confirm metabolites as quickly as
possible, and the workflows designed for the TripleTOF system
enable the fast, accurate metabolic profiling necessary for both
ADMET and DMPK studies. The collection of information-rich
data using a specialized quant/qual workflow (Figure 5) and
the selective extraction of drug-related information during data
acquisition produce highly-relevant structural information for
detection and characterization of metabolites in the same run.
The TripleTOF system has the capacity to collect both MS and
MS/MS scan data simultaneously; whilst survey scans generate
TOF-MS spectra, a real-time filter based on mass defects
(RT-MMDF) triggers MS/MS acquisition of peaks similar to the
parent drug, rather than on unrelated background noise, so
that only highly relevant information is exhibited in the resulting
spectra. Another feature of the hybrid instrumentation is the
combination of high resolution and high detector speed that can
maintain sufficiently high resolution at low m/z, thereby including
even more fragments in the equation for unambiguous structural
assignment. (For a full listing of TripleTOF system features, see
Table 5.) Altogether, intelligent data acquisition, simultaneous
quant/qual analyses, and high scan speeds packaged in one hybrid
instrument can support these powerful metabolite workflows.
DRUG METABOLISM
11
2) All-in-one, data-acquisition workflows for comprehensive
metabolite profiling: SWATH Acquisition and the
TripleTOF system
The concept of a real-time algorithm for multiple mass defect filtering
•
•
•
•
•
•
Regarded as separate from data-processing algorithms
Eliminates MS/MS-triggering on background noise
Determines which ion(s) are significantly changing with time
Selects the best ion(s) to target for MS/MS acquisition
Applied during UPLC/MS acquisition
Exists as part of information-dependent data acquisition (IDA) logic
Table 5: Key features of TripleTOF® System workflows for metabolite profiling
Coupled with MetabolitePilot Software, the TripleTOF system can
alleviate the data analysis bottlenecks traditionally associated with
metabolite discovery by supplying one integrated platform for
all metabolite profiling tasks, including metabolite identification,
confirmation of metabolic site, time-course tracking, and
inter-species metabolite comparisons. Offering one software
package simplifies and automates the interpretation of complex,
unwieldy data sets for the fast deconstruction of metabolite
information that is crucial to compound optimization. The
multifaceted quant/qual workflow has the flexibility and versatility
to deliver metabolite information needed for each stage of
drug development:
•
•
•
Early drug discovery: High-throughput quant/qual assays can quickly
analyze microsomal clearance data, matching fragments to proposed
structures to determine critical soft spot information, as well as
quantitating parent drug concentrations to establish metabolic stability
— all using a generic acquisition methodology in a single run.
Late stage discovery: The wide-ranging approaches for data filtration
(e.g., neutral loss, product ions, mass defect, isotope patterns) provide
multiple methods for the discovery of expected and unexpected
metabolites, even low-level, and phase II reactive metabolites. Accurate
mass, automated structure-driven processing, and weighted scoring
of structures speeds confidence in determining the site of metabolism.
Automated correlation of peak areas with analog data delivers
easier relative quantitation of metabolite and parent peaks for time
course studies.
Development stage: Processing of accurate mass and high resolution
data, low range mass accuracy, and isotope patterns accelerate
fragment assignment for definitive structural elucidation and
correlation. Integrated processing of pharmacokinetic batch data using
MultiQuant Software reveals metabolite concentrations and kinetic
profiles relative to the parent, relying on the fast scanning speeds and
the high sensitivity of the TripleTOF system to maintain resolution and
to detect minor, low-level metabolites.
SWATH Acquisition takes its name from the narrow precursor
mass range (or swath) of ions selected in Q1 for advancement
to Q2 for collision-induced fragmentation (Figure 3). Relying
on the fast scan speed of the TripleTOF 5600 system to
sequentially process a broad aggregate of narrow mass ranges
in a small amount of time, SWATH Acquisition generates a
comprehensive map of MS/MS spectra for every ion at every
time point. Effectively simplified using PCVG-filtering and other
post-acquisition data mining tools, these complex SWATH
Acquisition data sets are transformed into spectra comprised
of peaks relevant to the parent drug. In this way, one of the
primary challenges of metabolite profiling is overcome – the
all-inclusive detection of metabolites, including unpredicted and
trace-level, is accomplished just one injection without the need
for specialized, sample-specific methods. Using data-independent
methods, such as SWATH Acquisition, provides accessible results
for more straightforward metabolite structure elucidation and
identification. A layer of selectivity is established when using
successive narrow mass windows to record MS/MS of the chosen
ions within, converting complex MSe spectra to simplified datasets
displaying relevant, drug-related peaks.
The applicability of SWATH Acquisition data to high-resolution
quantitation is extended when data is collected using a TripleTOF
5600 system. Using MRM-style methods to gather single product
ion peak areas or to sum multiple product ions, metabolite
quantitation can be accomplished with the same sensitivity
and the dynamic ranges as those achieved on leading triple
quadrupole mass spectrometers. High-resolution acquisition
lends additional weight to the data quality by improving the
completeness of MS/MS data sets for structural elucidation
and permitting narrower mass windows to be used for peak
selection, removing potentially confounding interferences from
the quantitation process. The impact that the SWATH Acquisition
workflow has on throughput and productivity during drug
discovery and development is unparalleled. Access to an
all-encompassing digital archive of complete MS/MS quantitative
and qualitative information for every peak at every time point
in a sample is valuable at every stage for increasing productivity,
throughput, and data quality. (For a review of the additional
features of SWATH Acquisition, see Table 6.)
Unique qualitative features
1.
2.
3.
RUO-MKT-01-1583-A
Another hybrid instrument, the QTRAP 6500 system, a unique
combination of triple quadrupole and linear ion trap, delivers
the enhanced speed and sensitivity needed for rapidly detecting
the most number of metabolites using the minimum number of
injections – an efficient approach that complements accurate
mass HRMS (see Table 4). During in vivo testing, drugs and
metabolites need to be detected at physiologically relevant
concentrations, which require an exceedingly sensitive approach.
Additionally, early discovery of toxic, reactive metabolites is
critical for the selection of appropriate candidates during drug
development. Building on the significant gains in sensitivity and
fast scanning speeds resulting from modern ion trap innovations,
pMRM methods find and confirm the presence of ultra-low level
in vivo and in vitro metabolites. These highly sensitive, targeted
experiments are based on the parent drug fragmentation pattern
and possible biotransformations to yield significantly more
metabolites, even from complex biological matrices. Reactive
metabolite screening experiments are also enabled by the high
scan speeds of the QTRAP system. Dual scan surveys (precursor
ion and neutral loss) can be conducted in one fast IDA cycle; and
when combined with positive/negative polarity switching, these
are the first workflows that can detect metabolites of varying
polarities in the same run with sufficient sensitivity and accuracy
for the highest level of confidence. In just a single injection,
these highly sensitive workflows, pMRM and the dual scan IDA
methods, provide the reassurance that an all-encompassing list of
even the most minor of metabolites has been generated.
Unique quant/qual characteristics
1. SelexION™ Technology drives quantitative workflows, but has the added benefit of
boosting qualitative techniques.
2. Other mobility techniques are qual focused, not quant.
a. SelexION technology’s pre-ion-source location maintains precursor selection and
fragmentation.
b. Ion mobility spectrometry (IMS) occurs in the collision cell and provides no selectivity
before Q1.
c. FAIMS, another ion-mobility-based spectrometry, displays reduced robustness and
reliability.
3. Chemical modifiers gives the flexibility to try various options for better separation.
4. SelexION technology is compatible with QTRAP and TripleTOF systems.
a. MRM-style application for quantitation (continuous flux of ions)
b. Full-scan QTOF (pulsed technique)
5. Easy switch between DMS on and off modes
a. Allows ions to be transmitted in transparent mode with voltages turned off
b. Easily installed in a few minutes without breaking vacuum or using any tools
Table 7: Key features of SelexION™ Technology for metabolite identification.
•
•
Early stage discovery: In combination with pMRM methods, polarity
switching and fast scanning can quickly and sensitively detect low-level,
transient metabolites, both predicted and unpredicted, in biological
samples. The detection of minor, but potentially highly toxic, reactive
metabolites by identifying glutathione-conjugates is very important
for early safety assessments. Building these compound specific
acquisition methods, acquiring the data, and then processing it can be
consolidated on just one platform – LightSight Software.
Late stage discovery: Highly sensitive pMRM scans used during
first-pass, in vitro and in vivo metabolic screening can quickly and
efficiently profile metabolites at clinically relevant concentrations, while
simultaneously generating quantitative data for metabolic stability
and pharmacokinetic studies. The high sensitivity of the linear ion trap
detects more trace metabolites and their fragments, which in turn
provide more information for structural assignments.
Unique quantitative features
Less complex MS/MS spectrum than traditional DIA techniques like MSe
Retention of isotope pattern for each fragment due to wider Q1 selection
• Supports 14C/SIL ADC metabolism studies
Capture of 100% MS/MS for comprehensive identification of minor metabolites/catabolites
1. Selective MS/MS quantification realized using MRM-style methods for measurement of single
product ions or for summing multiple product ions.
2. Multicomponent quantification is feasible with this single-acquisition method.
• Quantification of total mAb, conjugated and free small molecule
Table 6: Key features of SWATH™ Acquisition on the TripleTOF® System for metabolite ID.
12
3) Cutting-edge workflows for detecting low-level
metabolites: QTRAP 6500 system and LightSight Software
INTRODUCTION
INTRODUCTION
For Research Use Only. Not for use in diagnostic procedures.
DRUG METABOLISM
Figure 4: An illustration of multiple mass defect ranges that were calculated in MetabolitePilot™ Software.
www.absciex.com
www.absciex.com
DRUG METABOLISM
13
2) All-in-one, data-acquisition workflows for comprehensive
metabolite profiling: SWATH Acquisition and the
TripleTOF system
The concept of a real-time algorithm for multiple mass defect filtering
•
•
•
•
•
•
Regarded as separate from data-processing algorithms
Eliminates MS/MS-triggering on background noise
Determines which ion(s) are significantly changing with time
Selects the best ion(s) to target for MS/MS acquisition
Applied during UPLC/MS acquisition
Exists as part of information-dependent data acquisition (IDA) logic
Table 5: Key features of TripleTOF® System workflows for metabolite profiling
Coupled with MetabolitePilot Software, the TripleTOF system can
alleviate the data analysis bottlenecks traditionally associated with
metabolite discovery by supplying one integrated platform for
all metabolite profiling tasks, including metabolite identification,
confirmation of metabolic site, time-course tracking, and
inter-species metabolite comparisons. Offering one software
package simplifies and automates the interpretation of complex,
unwieldy data sets for the fast deconstruction of metabolite
information that is crucial to compound optimization. The
multifaceted quant/qual workflow has the flexibility and versatility
to deliver metabolite information needed for each stage of
drug development:
•
•
•
Early drug discovery: High-throughput quant/qual assays can quickly
analyze microsomal clearance data, matching fragments to proposed
structures to determine critical soft spot information, as well as
quantitating parent drug concentrations to establish metabolic stability
— all using a generic acquisition methodology in a single run.
Late stage discovery: The wide-ranging approaches for data filtration
(e.g., neutral loss, product ions, mass defect, isotope patterns) provide
multiple methods for the discovery of expected and unexpected
metabolites, even low-level, and phase II reactive metabolites. Accurate
mass, automated structure-driven processing, and weighted scoring
of structures speeds confidence in determining the site of metabolism.
Automated correlation of peak areas with analog data delivers
easier relative quantitation of metabolite and parent peaks for time
course studies.
Development stage: Processing of accurate mass and high resolution
data, low range mass accuracy, and isotope patterns accelerate
fragment assignment for definitive structural elucidation and
correlation. Integrated processing of pharmacokinetic batch data using
MultiQuant Software reveals metabolite concentrations and kinetic
profiles relative to the parent, relying on the fast scanning speeds and
the high sensitivity of the TripleTOF system to maintain resolution and
to detect minor, low-level metabolites.
SWATH Acquisition takes its name from the narrow precursor
mass range (or swath) of ions selected in Q1 for advancement
to Q2 for collision-induced fragmentation (Figure 3). Relying
on the fast scan speed of the TripleTOF 5600 system to
sequentially process a broad aggregate of narrow mass ranges
in a small amount of time, SWATH Acquisition generates a
comprehensive map of MS/MS spectra for every ion at every
time point. Effectively simplified using PCVG-filtering and other
post-acquisition data mining tools, these complex SWATH
Acquisition data sets are transformed into spectra comprised
of peaks relevant to the parent drug. In this way, one of the
primary challenges of metabolite profiling is overcome – the
all-inclusive detection of metabolites, including unpredicted and
trace-level, is accomplished just one injection without the need
for specialized, sample-specific methods. Using data-independent
methods, such as SWATH Acquisition, provides accessible results
for more straightforward metabolite structure elucidation and
identification. A layer of selectivity is established when using
successive narrow mass windows to record MS/MS of the chosen
ions within, converting complex MSe spectra to simplified datasets
displaying relevant, drug-related peaks.
The applicability of SWATH Acquisition data to high-resolution
quantitation is extended when data is collected using a TripleTOF
5600 system. Using MRM-style methods to gather single product
ion peak areas or to sum multiple product ions, metabolite
quantitation can be accomplished with the same sensitivity
and the dynamic ranges as those achieved on leading triple
quadrupole mass spectrometers. High-resolution acquisition
lends additional weight to the data quality by improving the
completeness of MS/MS data sets for structural elucidation
and permitting narrower mass windows to be used for peak
selection, removing potentially confounding interferences from
the quantitation process. The impact that the SWATH Acquisition
workflow has on throughput and productivity during drug
discovery and development is unparalleled. Access to an
all-encompassing digital archive of complete MS/MS quantitative
and qualitative information for every peak at every time point
in a sample is valuable at every stage for increasing productivity,
throughput, and data quality. (For a review of the additional
features of SWATH Acquisition, see Table 6.)
Unique qualitative features
1.
2.
3.
RUO-MKT-01-1583-A
Another hybrid instrument, the QTRAP 6500 system, a unique
combination of triple quadrupole and linear ion trap, delivers
the enhanced speed and sensitivity needed for rapidly detecting
the most number of metabolites using the minimum number of
injections – an efficient approach that complements accurate
mass HRMS (see Table 4). During in vivo testing, drugs and
metabolites need to be detected at physiologically relevant
concentrations, which require an exceedingly sensitive approach.
Additionally, early discovery of toxic, reactive metabolites is
critical for the selection of appropriate candidates during drug
development. Building on the significant gains in sensitivity and
fast scanning speeds resulting from modern ion trap innovations,
pMRM methods find and confirm the presence of ultra-low level
in vivo and in vitro metabolites. These highly sensitive, targeted
experiments are based on the parent drug fragmentation pattern
and possible biotransformations to yield significantly more
metabolites, even from complex biological matrices. Reactive
metabolite screening experiments are also enabled by the high
scan speeds of the QTRAP system. Dual scan surveys (precursor
ion and neutral loss) can be conducted in one fast IDA cycle; and
when combined with positive/negative polarity switching, these
are the first workflows that can detect metabolites of varying
polarities in the same run with sufficient sensitivity and accuracy
for the highest level of confidence. In just a single injection,
these highly sensitive workflows, pMRM and the dual scan IDA
methods, provide the reassurance that an all-encompassing list of
even the most minor of metabolites has been generated.
Unique quant/qual characteristics
1. SelexION™ Technology drives quantitative workflows, but has the added benefit of
boosting qualitative techniques.
2. Other mobility techniques are qual focused, not quant.
a. SelexION technology’s pre-ion-source location maintains precursor selection and
fragmentation.
b. Ion mobility spectrometry (IMS) occurs in the collision cell and provides no selectivity
before Q1.
c. FAIMS, another ion-mobility-based spectrometry, displays reduced robustness and
reliability.
3. Chemical modifiers gives the flexibility to try various options for better separation.
4. SelexION technology is compatible with QTRAP and TripleTOF systems.
a. MRM-style application for quantitation (continuous flux of ions)
b. Full-scan QTOF (pulsed technique)
5. Easy switch between DMS on and off modes
a. Allows ions to be transmitted in transparent mode with voltages turned off
b. Easily installed in a few minutes without breaking vacuum or using any tools
Table 7: Key features of SelexION™ Technology for metabolite identification.
•
•
Early stage discovery: In combination with pMRM methods, polarity
switching and fast scanning can quickly and sensitively detect low-level,
transient metabolites, both predicted and unpredicted, in biological
samples. The detection of minor, but potentially highly toxic, reactive
metabolites by identifying glutathione-conjugates is very important
for early safety assessments. Building these compound specific
acquisition methods, acquiring the data, and then processing it can be
consolidated on just one platform – LightSight Software.
Late stage discovery: Highly sensitive pMRM scans used during
first-pass, in vitro and in vivo metabolic screening can quickly and
efficiently profile metabolites at clinically relevant concentrations, while
simultaneously generating quantitative data for metabolic stability
and pharmacokinetic studies. The high sensitivity of the linear ion trap
detects more trace metabolites and their fragments, which in turn
provide more information for structural assignments.
Unique quantitative features
Less complex MS/MS spectrum than traditional DIA techniques like MSe
Retention of isotope pattern for each fragment due to wider Q1 selection
• Supports 14C/SIL ADC metabolism studies
Capture of 100% MS/MS for comprehensive identification of minor metabolites/catabolites
1. Selective MS/MS quantification realized using MRM-style methods for measurement of single
product ions or for summing multiple product ions.
2. Multicomponent quantification is feasible with this single-acquisition method.
• Quantification of total mAb, conjugated and free small molecule
Table 6: Key features of SWATH™ Acquisition on the TripleTOF® System for metabolite ID.
12
3) Cutting-edge workflows for detecting low-level
metabolites: QTRAP 6500 system and LightSight Software
INTRODUCTION
INTRODUCTION
For Research Use Only. Not for use in diagnostic procedures.
DRUG METABOLISM
Figure 4: An illustration of multiple mass defect ranges that were calculated in MetabolitePilot™ Software.
www.absciex.com
www.absciex.com
DRUG METABOLISM
13
For Research Use Only. Not for use in diagnostic procedures.
INTRODUCTION
Figure 5: A diagram outlining quant/qual workflows conducted on the TripleTOF®
5600 System.
•
Development stage: LightSight Software integrates UV or radiolabel
quantitation data with MS and MS/MS relative peak areas into one
report alongside structure elucidation to provide the relative abundances
of metabolites without a reference standard for pharmacokinetic
analysis. The sensitivity and speed improvements of an MS/MS
workflow are suitable for micro-dosing studies, which permit insight
into human pharmacokinetic studies prior to clinical research studies.
4) Eliminating isobaric background and separating isomeric
metabolites: SelexION technology workflows
Separation of isomeric metabolites and co-eluting, isobaric
interferences without the need for longer chromatographic run
times and expensive columns is especially challenging during
early ADME and definitive metabolite identification workflows,
where unwanted, overlapping background can significantly affect
the signal-to-noise ratio of quantitative experiments. SelexION
technology is an orthogonal, gas-phase separation technique that
filters hard-to-separate metabolites that have same molecular
weight but different spatial orientation (e.g., the positional
isomers of oxidized metabolites, demethylation products, and
glucuronide metabolites) and removes co-eluting contaminants
that arise during high-throughput UHPLC workflows. Mounted
at the entrance to the mass spectrometer, SelexION technology is
positioned for the maximum impact on quantitative workflows,
while still enabling precursor selection and fragmentation requisite
for qualitative analysis during preclinical toxicological safety and
efficacy testing. One of the major bottlenecks during definitive
metabolite identification during the drug development stage is the
separation of isomeric metabolites from complex, in vivo matrices;
SelexION technology optimizes the selectivity of these workflows,
while at the same time improving the signal for an enhanced
signal-to-noise reading. (See Table 7, for additional SelexION
technology features.) SelexION technology, compatible with both
TripleTOF and QTRAP systems, helps alleviate the separation
challenges of a high-throughput environment, with the versatility
and ease expected for enhancing productivity.
14
RUO-MKT-01-1583-A
INTRODUCTION
Perspectives for the future
Innovation has expedited the process of screening for drug
candidates by achieving higher levels of productivity and
automation, anticipating that increased efficiency will allow for
more resources to be directed towards new scientific research.
The improvements to HRMS performance – higher sensitivity
along with higher resolution and mass accuracy at faster scanning
speeds – and advances to rapid data acquisition methods have
significantly improved the feasibility of drug metabolism studies
in complex cellular backgrounds. By using data mining techniques
for discriminating drug-related material from background, it is
now possible to obtain high quality MS/MS spectra needed for
good structural determination without the need to customize
the acquisition of each, specific peak, making HRMS-based
workflows the technique of choice for high-throughput drug
discovery environments.
To keep pace with the changing demands of drug discovery
and development, AB SCIEX offers innovative high-performance
instruments and workhorse software applications that allow for
multifaceted metabolite identification tasks to be consolidated
into one workflow (Figure 6). Quant/qual workflows conducted
on the TripleTOF 5600 system and the QTRAP 6500 system
provide the sensitivity and selectivity to detect trace-levels of
metabolites in one injection. RT-MMDF algorithms add efficiency
to the process of discriminating drug-related material from
background noise, ensuring that a comprehensive collection
of MS/MS fragments is obtained for structural assignment
without sample re-injection. SWATH Acquisition also relies on a
transformative algorithm to extract relevant fragment information
from data acquired using a compound-independent approach –
to ensure that no drug-related material is overlooked. Together,
this technology leads the way for the finding, identifying,
and confirming metabolites as quickly as possible, without
compromising throughput.
Even as the extraction of relevant drug-related peaks become
increasingly efficient and accessible due to highly-selective
filtering techniques and HRMS instrument enhancements,
the true bottleneck in metabolite profiling remains in the fast
and effective assignment of structure in the discovery stage,
a process that is now very manual and hands-on, requiring
expert interpretation and input. Progress in this area has been
encouraging. Structure databases and software packages
have begun to incorporate accurate mass HRMS and MS/MS
data information to support automated metabolite structure
predictions and localization of the site of metabolism from
product ion spectra.4,6 MetabolitePilot Software, for example,
includes functionality that assists in (but does not fully automate)
the assignment of metabolite structures. As breakthroughs in
automation of structural assignment are achieved, hands-on
review of the data will ultimately still be needed by experienced
scientists, but the number of compounds analyzed will grow
exponentially, ultimately unleashing drug development pathways
DRUG METABOLISM
www.absciex.com
Figure 6: Complementary workflows and complete product portfolios available to streamline drug metabolism workflows and to address key challenges.
from this long-standing barrier to throughput. Looking to the
future, structure elucidation will emerge as a fully automated
solution, integrated with intelligently-filtered HRSM data and an
integrated cheminformatics approach that predicts structures
based on known biotransformation pathways, so that finding,
identifying, and confirming metabolites is accelerated to a whole
new level of productivity.
Chemistry. 2014; 86(2):1202-9.
4
Pahler A, Brink A. “Software aided approaches to structure-based metabolite identification in
drug discovery and development.” Drug Discovery Today: Technologies. 2013; 10 (1): 207-217.
5
Zhu, M, Zhang, H, Humphreys WG. “Drug metabolite profiling and identification by highresolution mass spectrometry.”J Biol Chem. 2011; 286 (29): 25419-25425.
6
Zelesky V, Schneider, R, Janiszewski J, Zamora I, Ferguson J, Troutman M. “Software automation
tools for increased throughput metabolic soft-spot identification in early drug discovery.”
Bioanalysis. 2013; 5(10): 1165-1179.
7
Erve JCL, Gauby S, Maynard, Jr. JW, Svensson MA, Tonn G, Quinn KP. “Bioactivation of sitaxentan
in liver microsomes, hepatocytes, and expressed human P450s with characterization of the
glutathione conjugate by liquid chromatography tandem mass spectrometry.” Chemical Research
in Toxicology. 2013; 26: 926-936.
8
Qiao S, Shi X, Shi R, Liu M, Liu T, Zhang K, Wang Q, Yao M, Zhang L. “Identification of urinary
metabolites of imperatorin with a single run on an LC/Triple TOF system based on multiple mass
defect filter data acquisition and multiple data mining techniques.” Anal Bioanal Chem. 2013;
405: 6721-6738.
9
Liu M, Zhao S, Wang Z, Wang Y, Liu T, Song L, Wang C, Wang H, Tu P. “Identification of
metabolites of deoxyschizandrin in rats by UPLC-Q-TOF-MS/MS based on multiple mass defect
filter data acquisition and multiple data processing techniques.” Journal of Chromatography B.
2014; 949-950:115-126.
10
Bateman KP, Cohen L, Emary B, Pucci V. “Standardized workflows for increasing efficiency and
productivity in discovery stage bioanalysis.” Bioanalysis. 2013; 5(14): 1783-1794.
Abbreviations used
MS/MS, tandem mass spectrometry; UHPLC, ultra-high pressure
liquid chromatography; HRMS, high-resolution mass spectrometry;
ADMET, adsorption, distribution, metabolism, excretion and
toxicity; pMRM, predictive multiple reaction monitoring; RT-MMDF,
real-time multiple mass defect filter; IDA, information dependent
acquisition; DMPK, drug metabolism and pharmacokinetics;
DMS, differential mobility spectroscopy; and PCVG, principal
components variable grouping.
References
1
Lin JH, Lu AYH. “Role of Pharmacokinetics and Metabolism in Drug Discovery and Development.”
Pharmacological Reviews. 1997; 49(4): 403-449.
2
Ma S, Chowdhury SK. “Data acquisition and data mining techniques for metabolite identification
using LC coupled to high-resolution MS.” Bioanalysis. 2013; 5(10): 1285-1297.
3
Zhu Z, Chen Y, Subramanian R. “Comparison of information-dependent acquisition, SWATH™
acquisition, and MSAll techniques in metabolite identification study employing ultrahighperformance liquid chromatography—quadrupole time-of-flight mass spectrometry.” Analytical
www.absciex.com
DRUG METABOLISM
15
For Research Use Only. Not for use in diagnostic procedures.
INTRODUCTION
Figure 5: A diagram outlining quant/qual workflows conducted on the TripleTOF®
5600 System.
•
Development stage: LightSight Software integrates UV or radiolabel
quantitation data with MS and MS/MS relative peak areas into one
report alongside structure elucidation to provide the relative abundances
of metabolites without a reference standard for pharmacokinetic
analysis. The sensitivity and speed improvements of an MS/MS
workflow are suitable for micro-dosing studies, which permit insight
into human pharmacokinetic studies prior to clinical research studies.
4) Eliminating isobaric background and separating isomeric
metabolites: SelexION technology workflows
Separation of isomeric metabolites and co-eluting, isobaric
interferences without the need for longer chromatographic run
times and expensive columns is especially challenging during
early ADME and definitive metabolite identification workflows,
where unwanted, overlapping background can significantly affect
the signal-to-noise ratio of quantitative experiments. SelexION
technology is an orthogonal, gas-phase separation technique that
filters hard-to-separate metabolites that have same molecular
weight but different spatial orientation (e.g., the positional
isomers of oxidized metabolites, demethylation products, and
glucuronide metabolites) and removes co-eluting contaminants
that arise during high-throughput UHPLC workflows. Mounted
at the entrance to the mass spectrometer, SelexION technology is
positioned for the maximum impact on quantitative workflows,
while still enabling precursor selection and fragmentation requisite
for qualitative analysis during preclinical toxicological safety and
efficacy testing. One of the major bottlenecks during definitive
metabolite identification during the drug development stage is the
separation of isomeric metabolites from complex, in vivo matrices;
SelexION technology optimizes the selectivity of these workflows,
while at the same time improving the signal for an enhanced
signal-to-noise reading. (See Table 7, for additional SelexION
technology features.) SelexION technology, compatible with both
TripleTOF and QTRAP systems, helps alleviate the separation
challenges of a high-throughput environment, with the versatility
and ease expected for enhancing productivity.
14
RUO-MKT-01-1583-A
INTRODUCTION
Perspectives for the future
Innovation has expedited the process of screening for drug
candidates by achieving higher levels of productivity and
automation, anticipating that increased efficiency will allow for
more resources to be directed towards new scientific research.
The improvements to HRMS performance – higher sensitivity
along with higher resolution and mass accuracy at faster scanning
speeds – and advances to rapid data acquisition methods have
significantly improved the feasibility of drug metabolism studies
in complex cellular backgrounds. By using data mining techniques
for discriminating drug-related material from background, it is
now possible to obtain high quality MS/MS spectra needed for
good structural determination without the need to customize
the acquisition of each, specific peak, making HRMS-based
workflows the technique of choice for high-throughput drug
discovery environments.
To keep pace with the changing demands of drug discovery
and development, AB SCIEX offers innovative high-performance
instruments and workhorse software applications that allow for
multifaceted metabolite identification tasks to be consolidated
into one workflow (Figure 6). Quant/qual workflows conducted
on the TripleTOF 5600 system and the QTRAP 6500 system
provide the sensitivity and selectivity to detect trace-levels of
metabolites in one injection. RT-MMDF algorithms add efficiency
to the process of discriminating drug-related material from
background noise, ensuring that a comprehensive collection
of MS/MS fragments is obtained for structural assignment
without sample re-injection. SWATH Acquisition also relies on a
transformative algorithm to extract relevant fragment information
from data acquired using a compound-independent approach –
to ensure that no drug-related material is overlooked. Together,
this technology leads the way for the finding, identifying,
and confirming metabolites as quickly as possible, without
compromising throughput.
Even as the extraction of relevant drug-related peaks become
increasingly efficient and accessible due to highly-selective
filtering techniques and HRMS instrument enhancements,
the true bottleneck in metabolite profiling remains in the fast
and effective assignment of structure in the discovery stage,
a process that is now very manual and hands-on, requiring
expert interpretation and input. Progress in this area has been
encouraging. Structure databases and software packages
have begun to incorporate accurate mass HRMS and MS/MS
data information to support automated metabolite structure
predictions and localization of the site of metabolism from
product ion spectra.4,6 MetabolitePilot Software, for example,
includes functionality that assists in (but does not fully automate)
the assignment of metabolite structures. As breakthroughs in
automation of structural assignment are achieved, hands-on
review of the data will ultimately still be needed by experienced
scientists, but the number of compounds analyzed will grow
exponentially, ultimately unleashing drug development pathways
DRUG METABOLISM
www.absciex.com
Figure 6: Complementary workflows and complete product portfolios available to streamline drug metabolism workflows and to address key challenges.
from this long-standing barrier to throughput. Looking to the
future, structure elucidation will emerge as a fully automated
solution, integrated with intelligently-filtered HRSM data and an
integrated cheminformatics approach that predicts structures
based on known biotransformation pathways, so that finding,
identifying, and confirming metabolites is accelerated to a whole
new level of productivity.
Chemistry. 2014; 86(2):1202-9.
4
Pahler A, Brink A. “Software aided approaches to structure-based metabolite identification in
drug discovery and development.” Drug Discovery Today: Technologies. 2013; 10 (1): 207-217.
5
Zhu, M, Zhang, H, Humphreys WG. “Drug metabolite profiling and identification by highresolution mass spectrometry.”J Biol Chem. 2011; 286 (29): 25419-25425.
6
Zelesky V, Schneider, R, Janiszewski J, Zamora I, Ferguson J, Troutman M. “Software automation
tools for increased throughput metabolic soft-spot identification in early drug discovery.”
Bioanalysis. 2013; 5(10): 1165-1179.
7
Erve JCL, Gauby S, Maynard, Jr. JW, Svensson MA, Tonn G, Quinn KP. “Bioactivation of sitaxentan
in liver microsomes, hepatocytes, and expressed human P450s with characterization of the
glutathione conjugate by liquid chromatography tandem mass spectrometry.” Chemical Research
in Toxicology. 2013; 26: 926-936.
8
Qiao S, Shi X, Shi R, Liu M, Liu T, Zhang K, Wang Q, Yao M, Zhang L. “Identification of urinary
metabolites of imperatorin with a single run on an LC/Triple TOF system based on multiple mass
defect filter data acquisition and multiple data mining techniques.” Anal Bioanal Chem. 2013;
405: 6721-6738.
9
Liu M, Zhao S, Wang Z, Wang Y, Liu T, Song L, Wang C, Wang H, Tu P. “Identification of
metabolites of deoxyschizandrin in rats by UPLC-Q-TOF-MS/MS based on multiple mass defect
filter data acquisition and multiple data processing techniques.” Journal of Chromatography B.
2014; 949-950:115-126.
10
Bateman KP, Cohen L, Emary B, Pucci V. “Standardized workflows for increasing efficiency and
productivity in discovery stage bioanalysis.” Bioanalysis. 2013; 5(14): 1783-1794.
Abbreviations used
MS/MS, tandem mass spectrometry; UHPLC, ultra-high pressure
liquid chromatography; HRMS, high-resolution mass spectrometry;
ADMET, adsorption, distribution, metabolism, excretion and
toxicity; pMRM, predictive multiple reaction monitoring; RT-MMDF,
real-time multiple mass defect filter; IDA, information dependent
acquisition; DMPK, drug metabolism and pharmacokinetics;
DMS, differential mobility spectroscopy; and PCVG, principal
components variable grouping.
References
1
Lin JH, Lu AYH. “Role of Pharmacokinetics and Metabolism in Drug Discovery and Development.”
Pharmacological Reviews. 1997; 49(4): 403-449.
2
Ma S, Chowdhury SK. “Data acquisition and data mining techniques for metabolite identification
using LC coupled to high-resolution MS.” Bioanalysis. 2013; 5(10): 1285-1297.
3
Zhu Z, Chen Y, Subramanian R. “Comparison of information-dependent acquisition, SWATH™
acquisition, and MSAll techniques in metabolite identification study employing ultrahighperformance liquid chromatography—quadrupole time-of-flight mass spectrometry.” Analytical
www.absciex.com
DRUG METABOLISM
15
Technology Drives High-Performance
Biomolecular Mass Spectrometry
Enhancing the sensitivity and dynamic range of the AB SCIEX QTRAP® 6500 System with IonDrive™ Technology
Bruce Thompson and Bruce Collings
AB SCIEX, Concord, ON, Canada
In applications that range from proteomics to biomarker
discovery to drug development, mass spectrometry has become
the tool that provides the highest accuracy and specificity
in trace chemical analysis. While there are many important
metrics of analytical performance (accuracy, precision, limit
of quantitation), they all rely heavily on two key instrumental
performance characteristics – sensitivity and dynamic range. In
mass spectrometry, instrument sensitivity can best be defined as
the number of ions detected per molecule of analyte injected,
thus accounting for all losses in ionization, transmission, and
detection. Dynamic range is usually defined as the range of linear
response of the instrument, limited at the low end by absolute
sensitivity and, at the high end, by detector or other instrumentrelated saturation effects.
Over the last thirty years of development at AB SCIEX, enormous
strides have been made to improve both the instrument sensitivity
and the dynamic range. This improved performance has enabled
new applications to be addressed by mass spectrometry and
has allowed analyses to be performed more rapidly and with
greater confidence and higher precision. Higher sensitivity has
also enabled the use of additional capabilities and techniques
that provide improved analytical specificity – such as higher mass
resolution, faster scans speeds, and shorter reaction monitoring
times – and techniques such as ion mobility/mass spectrometry
Figure 1: The growth in sensitivity of high-flow LC/MS/MS mass spectrometer systems
over the last thirty years at AB SCIEX.
16
RUO-MKT-01-1583-A
However, the sampling aperture into the vacuum still typically
represents the largest area of ion losses. We have, therefore,
increased the size of the orifice in order to sample more ions.
Improved pumping in the interface helps maintain an acceptable
core vacuum pressure without increasing the size of the turbo
pumps. The gas expanding through the orifice forms a supersonic
free jet with a characteristic barrel shock structure as shown
in Figure 2. The high gas flow and pressure provide a strong
drag force on the ions that are entrained in this jet, making it
more challenging to effectively focus the ions through the next
aperture. The new IonDrive™ QJet Ion Drive optics employs a
two-stage RF quadrupole to capture and focus the ions to the
centerline of the optics using the technique of collisional focusing,
allowing the majority of gas to be pumped away. The first
section is a large-diameter, RF-only quadrupole with narrow gaps
between the rods in order to contain the ions. The narrow gaps
minimize the radial outflow of gas, and, therefore, ion losses,
while allowing the entrained ions to become collisionally focused.
The second section is a smaller diameter quadrupole that provides
the final stage of ion-beam focusing while the gas escapes.
The transmission efficiency of the ions into the next chamber is
approximately 50%, an impressive figure considering the larger
orifice diameter, higher pressure, and higher gas velocity.
TECHNOLOGY SPOTLIGHT
TECHNOLOGY SPOTLIGHT
For Research Use Only. Not for use in diagnostic procedures.
Figure 2: Cross-sectional view of the IonDrive™ QJet Ion Guide showing the supersonic
free jet (supersonic flow region in red) and the gas flow along the axis.
combinations added levels of tandem mass spectrometry
(MS/MS/MS). The growth curve of sensitivity in AB SCIEX triple
quadrupole mass spectrometers over this time period is plotted
in Figure 1, which shows a growth of nearly six orders of
magnitude in absolute sensitivity since the first AB SCIEX
LC/MS/MS product, the TAGA 6000. The AB SCIEX QTRAP®
6500 System, our newest and highest-performance instrument,
reaches new levels in both sensitivity and dynamic range. New
technologies in both the ion optics and ion counting detection
system have driven these performance increases.
A key step in achieving higher sensitivity is to create more ions in
the source. Over the years, improvements in ionization efficiency
have been achieved by increasing the efficiency of desolvation
and declustering in the source. The new IonDrive™ Turbo V
Source of the QTRAP 6500 system has reached a new level. By
optimizing the design of the IonDrive Turbo V source heaters for
better and more uniform distribution of heat in the region of
droplet evaporation, the efficiency of creating ions in front of the
sampling orifice has improved, especially at higher liquid flow
rates and for less volatile compounds.
DRUG METABOLISM
www.absciex.com
Figure 3: Dynamic range of the high energy conversion dynode (HED) detection system compared to the standard channel-electron multiplier (CEM) detection system.
www.absciex.com
DRUG METABOLISM
17
Technology Drives High-Performance
Biomolecular Mass Spectrometry
Enhancing the sensitivity and dynamic range of the AB SCIEX QTRAP® 6500 System with IonDrive™ Technology
Bruce Thompson and Bruce Collings
AB SCIEX, Concord, ON, Canada
In applications that range from proteomics to biomarker
discovery to drug development, mass spectrometry has become
the tool that provides the highest accuracy and specificity
in trace chemical analysis. While there are many important
metrics of analytical performance (accuracy, precision, limit
of quantitation), they all rely heavily on two key instrumental
performance characteristics – sensitivity and dynamic range. In
mass spectrometry, instrument sensitivity can best be defined as
the number of ions detected per molecule of analyte injected,
thus accounting for all losses in ionization, transmission, and
detection. Dynamic range is usually defined as the range of linear
response of the instrument, limited at the low end by absolute
sensitivity and, at the high end, by detector or other instrumentrelated saturation effects.
Over the last thirty years of development at AB SCIEX, enormous
strides have been made to improve both the instrument sensitivity
and the dynamic range. This improved performance has enabled
new applications to be addressed by mass spectrometry and
has allowed analyses to be performed more rapidly and with
greater confidence and higher precision. Higher sensitivity has
also enabled the use of additional capabilities and techniques
that provide improved analytical specificity – such as higher mass
resolution, faster scans speeds, and shorter reaction monitoring
times – and techniques such as ion mobility/mass spectrometry
Figure 1: The growth in sensitivity of high-flow LC/MS/MS mass spectrometer systems
over the last thirty years at AB SCIEX.
16
RUO-MKT-01-1583-A
However, the sampling aperture into the vacuum still typically
represents the largest area of ion losses. We have, therefore,
increased the size of the orifice in order to sample more ions.
Improved pumping in the interface helps maintain an acceptable
core vacuum pressure without increasing the size of the turbo
pumps. The gas expanding through the orifice forms a supersonic
free jet with a characteristic barrel shock structure as shown
in Figure 2. The high gas flow and pressure provide a strong
drag force on the ions that are entrained in this jet, making it
more challenging to effectively focus the ions through the next
aperture. The new IonDrive™ QJet Ion Drive optics employs a
two-stage RF quadrupole to capture and focus the ions to the
centerline of the optics using the technique of collisional focusing,
allowing the majority of gas to be pumped away. The first
section is a large-diameter, RF-only quadrupole with narrow gaps
between the rods in order to contain the ions. The narrow gaps
minimize the radial outflow of gas, and, therefore, ion losses,
while allowing the entrained ions to become collisionally focused.
The second section is a smaller diameter quadrupole that provides
the final stage of ion-beam focusing while the gas escapes.
The transmission efficiency of the ions into the next chamber is
approximately 50%, an impressive figure considering the larger
orifice diameter, higher pressure, and higher gas velocity.
TECHNOLOGY SPOTLIGHT
TECHNOLOGY SPOTLIGHT
For Research Use Only. Not for use in diagnostic procedures.
Figure 2: Cross-sectional view of the IonDrive™ QJet Ion Guide showing the supersonic
free jet (supersonic flow region in red) and the gas flow along the axis.
combinations added levels of tandem mass spectrometry
(MS/MS/MS). The growth curve of sensitivity in AB SCIEX triple
quadrupole mass spectrometers over this time period is plotted
in Figure 1, which shows a growth of nearly six orders of
magnitude in absolute sensitivity since the first AB SCIEX
LC/MS/MS product, the TAGA 6000. The AB SCIEX QTRAP®
6500 System, our newest and highest-performance instrument,
reaches new levels in both sensitivity and dynamic range. New
technologies in both the ion optics and ion counting detection
system have driven these performance increases.
A key step in achieving higher sensitivity is to create more ions in
the source. Over the years, improvements in ionization efficiency
have been achieved by increasing the efficiency of desolvation
and declustering in the source. The new IonDrive™ Turbo V
Source of the QTRAP 6500 system has reached a new level. By
optimizing the design of the IonDrive Turbo V source heaters for
better and more uniform distribution of heat in the region of
droplet evaporation, the efficiency of creating ions in front of the
sampling orifice has improved, especially at higher liquid flow
rates and for less volatile compounds.
DRUG METABOLISM
www.absciex.com
Figure 3: Dynamic range of the high energy conversion dynode (HED) detection system compared to the standard channel-electron multiplier (CEM) detection system.
www.absciex.com
DRUG METABOLISM
17
The increased rate of ion generation in the source and improved
transmission efficiency in the ion optics results in a higher ion
flux reaching the detector for a given amount of sample injected.
At the detector, ions are detected and registered with very high
efficiency using a pulse counting system with a very low noise
level. The challenge with pulse counting has always been the
measurement of high ion signals without saturation. The new
high energy conversion dynode (HED) detection system powered
by IonDrive™ Technology provides a very significant improvement
in this area, extending the upper level of ion counting while
maintaining the ability to register single ion events for the best
signal-to-noise ratios at the detection limit. The improved dynamic
range can be seen in Figure 3, which compares the new HED
detection system to the standard channel-electron multiplier
(CEM) detection system.
In Figure 3, the measured count rate of the first isotope of
reserpine is plotted against the true count rate as determined
from the known ratio and intensity of its fourth isotope. The
new system uses high-energy, ion-to-electron conversion and
a low-impedance, multi-channel continuous dynode detector
with a closely coupled transimpedance amplifier system that
allows high count rates to be sustained without loss of signal.
Arrival rates of up to 200 million ions per second can be
achieved resulting in a detector linear dynamic range of more
than six orders of magnitude. With the sensitivity and dynamic
range improvement described above, the QTRAP 6500 system
provides a new level of analytical performance, as evidenced
by the ability to detect and quantify sub-femtogram amounts
of biomolecules injected on-column as shown in Figure 4.
Demands for ever decreasing detection limits will continue to
drive the need for newer and better methods of ionization,
transmission, and detection in the future. However, the growth
curve of sensitivity will become more and more difficult to
maintain as we approach the limit of measuring and detecting
nearly every ion injected.
18
RUO-MKT-01-1583-A
TECHNOLOGY SPOTLIGHT
TECHNOLOGY SPOTLIGHT
For Research Use Only. Not for use in diagnostic procedures.
Figure 4: Signal from 500 attograms of verapamil injected on-column monitored in
MRM mode using the transition 455/165.
DRUG METABOLISM
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www.absciex.com
DRUG METABOLISM
19
The increased rate of ion generation in the source and improved
transmission efficiency in the ion optics results in a higher ion
flux reaching the detector for a given amount of sample injected.
At the detector, ions are detected and registered with very high
efficiency using a pulse counting system with a very low noise
level. The challenge with pulse counting has always been the
measurement of high ion signals without saturation. The new
high energy conversion dynode (HED) detection system powered
by IonDrive™ Technology provides a very significant improvement
in this area, extending the upper level of ion counting while
maintaining the ability to register single ion events for the best
signal-to-noise ratios at the detection limit. The improved dynamic
range can be seen in Figure 3, which compares the new HED
detection system to the standard channel-electron multiplier
(CEM) detection system.
In Figure 3, the measured count rate of the first isotope of
reserpine is plotted against the true count rate as determined
from the known ratio and intensity of its fourth isotope. The
new system uses high-energy, ion-to-electron conversion and
a low-impedance, multi-channel continuous dynode detector
with a closely coupled transimpedance amplifier system that
allows high count rates to be sustained without loss of signal.
Arrival rates of up to 200 million ions per second can be
achieved resulting in a detector linear dynamic range of more
than six orders of magnitude. With the sensitivity and dynamic
range improvement described above, the QTRAP 6500 system
provides a new level of analytical performance, as evidenced
by the ability to detect and quantify sub-femtogram amounts
of biomolecules injected on-column as shown in Figure 4.
Demands for ever decreasing detection limits will continue to
drive the need for newer and better methods of ionization,
transmission, and detection in the future. However, the growth
curve of sensitivity will become more and more difficult to
maintain as we approach the limit of measuring and detecting
nearly every ion injected.
18
RUO-MKT-01-1583-A
TECHNOLOGY SPOTLIGHT
TECHNOLOGY SPOTLIGHT
For Research Use Only. Not for use in diagnostic procedures.
Figure 4: Signal from 500 attograms of verapamil injected on-column monitored in
MRM mode using the transition 455/165.
DRUG METABOLISM
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DRUG METABOLISM
19
High-Resolution Time-of-Flight MS for
High-Quality Quantitative Analysis
Yves Leblanc
AB SCIEX, Concord, ON, Canada
For many years, quantitative analysis of compounds was
conducted on triple quadrupole (QqQ) mass spectrometry
(MS) instruments, while qualitative analysis was achieved on
mass spectrometry (MS) instruments with high resolution and
mass accuracy, such as the time-of-flight Q-ToF MS sytems. As
managers are looking into improving the efficiency of laboratory
practices and streamlining their decision-making processes
for projects ranging from drug metabolism to proteomics,
instruments that provide both qualitative and quantitative data
simultaneously are strongly desired. Examination of the attributes
of MS systems that deliver typical quantitative analysis, reveals the
following features: high duty cycle, high sensitivity, high selectivity,
and a wide dynamic range. In addition to the innovative front-end
developments associated with QqQ systems such as the Turbo V™
Source and the QJet® Ion Guide, key technologies were integrated
into the TripleTOF® 5600 System that enabled high-performance
quantitative analysis. First, operating the accelerator/pulser at
30 kHz provides high duty-cycle extraction of the ion beam exiting
the collision cell. To match this capability, a 40 GHz multichannel
time-to-digital (TDC) detection system that ensures a high rate
data collection was integrated into the system. Secondly, the
instrument operating at 15 kV TOF acceleration voltage with high
transmission grids (~92% transparency) assisted in maintaining
the sensitivity gains from the front-end changes. Both of these
technologies ensure high-efficiency extraction of the ion beam to
provide high sensitivity. On the qualitative front, improving system
performance in terms of resolution as well as mass accuracy was
important. To enhance resolution greater than 30K, the ion optic
was optimized to transfer ions with coherent ion trajectories
in the pulsar region over a distance of 2.5 m. The last key
improvement was to ensure the maintenance of a mass accuracy
< 2 ppm RMS over long periods of analysis. With the TripleTOF
5600 system, this is done in two steps: 1) the mass accuracy is
established with a scheduled calibration with each batch, and
2) the mass precision is maintained by dynamic monitoring
of background ions by adapting ion detection to variations in
analysis conditions.
As the XIC width is reduced from unit resolution (0.7 Da to mimic
quadrupole isolation) to 10 mDa, all interferences are eliminated
and a single LC peak is detected. This approach can be further
extended to peptide analysis by summing the signal associated
with both the charge state distribution as well as the isotope
distribution for each ion. Figure 2 shows the observed isotope
distribution associated with the +3 and +4 ions of neuromedin U
(NMU, seq.: YKVNEYQGPVAPSGGFFLFRPRN). Here, the isotopes
contribute to a large portion of the ion signal and each one of
them can be combined to give the appropriate signal-to-noise
(S/N) for selectivity and proper detection of LC peaks. When
neuromedin U was spiked in protein-precipitated plasma, more
than 80% of the signal was captured from the top four isotopes.
This approach was shown to improve linearity and precision for
the detection of NMU.
An additional benefit of the TripleTOF 5600 is the ability to
record MS/MS spectra retrospectively after separation by liquid
chromatography. This mode of operation is referred to as
MRM-HR where selective fragment(s) represent the peptide
peak. Figure 4 shows an example of the extraction of multiple
fragment ions associated a given peptide, in this particular case,
a phosphorylated peptide. This allows the experimental mass
spectrum to be compared to library entries to ensure that the
proper peptide was detected.
TECHNOLOGY SPOTLIGHT
TECHNOLOGY SPOTLIGHT
For Research Use Only. Not for use in diagnostic procedures.
With the ability to collect data at rates as high as 50 Hz in either
MS or MS/MS mode, the TripleTOF 5600 system offers unique
way to support both qualitative and quantitative analysis. First
and foremost, the TripleTOF 5600 system is compatible with UPLC
separation, and more than 12 data points across most LC peaks
can be collected. Secondly, the high resolution (> 30K) and the
high mass stability (< 2 ppm rms) provides the ability to extract a
narrow ion chromatogram (< 10 mDa) to achieve selectivity in
MS mode that is comparable to MRM analysis on QqQ systems.
The advantage of this approach is that generic data acquisition
can be used for analysis, thus the need for tuning. Equally
important is the ability to obtain reliable peak area from narrow
extracted ion chromatograms (XIC), which is indicative of the
instrument stability in terms of mass accuracy. Figure 1 shows
the benefit of reducing the XIC width in order to gain selectivity
in the detection of verapamil in diluted urine.
Figure 1: Analysis of verapamil in urine samples diluted 2-fold. Data was collected at
30K resolution in MS mode. The width of the XIC was set to mimic single quadrupole
analysis (XIC = 0.7 Da) and high resolution mode (XIC = 10 mDa).
Figure 2: Isotope distribution of neuromedin U for the dominant charge states (Z=+3 and Z=+4). When the highlighted isotopes are extracted and combined, they represent 91% of
the peptide signal, thus improving the overall sensitivity of the system.
20
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
21
High-Resolution Time-of-Flight MS for
High-Quality Quantitative Analysis
Yves Leblanc
AB SCIEX, Concord, ON, Canada
For many years, quantitative analysis of compounds was
conducted on triple quadrupole (QqQ) mass spectrometry
(MS) instruments, while qualitative analysis was achieved on
mass spectrometry (MS) instruments with high resolution and
mass accuracy, such as the time-of-flight Q-ToF MS sytems. As
managers are looking into improving the efficiency of laboratory
practices and streamlining their decision-making processes
for projects ranging from drug metabolism to proteomics,
instruments that provide both qualitative and quantitative data
simultaneously are strongly desired. Examination of the attributes
of MS systems that deliver typical quantitative analysis, reveals the
following features: high duty cycle, high sensitivity, high selectivity,
and a wide dynamic range. In addition to the innovative front-end
developments associated with QqQ systems such as the Turbo V™
Source and the QJet® Ion Guide, key technologies were integrated
into the TripleTOF® 5600 System that enabled high-performance
quantitative analysis. First, operating the accelerator/pulser at
30 kHz provides high duty-cycle extraction of the ion beam exiting
the collision cell. To match this capability, a 40 GHz multichannel
time-to-digital (TDC) detection system that ensures a high rate
data collection was integrated into the system. Secondly, the
instrument operating at 15 kV TOF acceleration voltage with high
transmission grids (~92% transparency) assisted in maintaining
the sensitivity gains from the front-end changes. Both of these
technologies ensure high-efficiency extraction of the ion beam to
provide high sensitivity. On the qualitative front, improving system
performance in terms of resolution as well as mass accuracy was
important. To enhance resolution greater than 30K, the ion optic
was optimized to transfer ions with coherent ion trajectories
in the pulsar region over a distance of 2.5 m. The last key
improvement was to ensure the maintenance of a mass accuracy
< 2 ppm RMS over long periods of analysis. With the TripleTOF
5600 system, this is done in two steps: 1) the mass accuracy is
established with a scheduled calibration with each batch, and
2) the mass precision is maintained by dynamic monitoring
of background ions by adapting ion detection to variations in
analysis conditions.
As the XIC width is reduced from unit resolution (0.7 Da to mimic
quadrupole isolation) to 10 mDa, all interferences are eliminated
and a single LC peak is detected. This approach can be further
extended to peptide analysis by summing the signal associated
with both the charge state distribution as well as the isotope
distribution for each ion. Figure 2 shows the observed isotope
distribution associated with the +3 and +4 ions of neuromedin U
(NMU, seq.: YKVNEYQGPVAPSGGFFLFRPRN). Here, the isotopes
contribute to a large portion of the ion signal and each one of
them can be combined to give the appropriate signal-to-noise
(S/N) for selectivity and proper detection of LC peaks. When
neuromedin U was spiked in protein-precipitated plasma, more
than 80% of the signal was captured from the top four isotopes.
This approach was shown to improve linearity and precision for
the detection of NMU.
An additional benefit of the TripleTOF 5600 is the ability to
record MS/MS spectra retrospectively after separation by liquid
chromatography. This mode of operation is referred to as
MRM-HR where selective fragment(s) represent the peptide
peak. Figure 4 shows an example of the extraction of multiple
fragment ions associated a given peptide, in this particular case,
a phosphorylated peptide. This allows the experimental mass
spectrum to be compared to library entries to ensure that the
proper peptide was detected.
TECHNOLOGY SPOTLIGHT
TECHNOLOGY SPOTLIGHT
For Research Use Only. Not for use in diagnostic procedures.
With the ability to collect data at rates as high as 50 Hz in either
MS or MS/MS mode, the TripleTOF 5600 system offers unique
way to support both qualitative and quantitative analysis. First
and foremost, the TripleTOF 5600 system is compatible with UPLC
separation, and more than 12 data points across most LC peaks
can be collected. Secondly, the high resolution (> 30K) and the
high mass stability (< 2 ppm rms) provides the ability to extract a
narrow ion chromatogram (< 10 mDa) to achieve selectivity in
MS mode that is comparable to MRM analysis on QqQ systems.
The advantage of this approach is that generic data acquisition
can be used for analysis, thus the need for tuning. Equally
important is the ability to obtain reliable peak area from narrow
extracted ion chromatograms (XIC), which is indicative of the
instrument stability in terms of mass accuracy. Figure 1 shows
the benefit of reducing the XIC width in order to gain selectivity
in the detection of verapamil in diluted urine.
Figure 1: Analysis of verapamil in urine samples diluted 2-fold. Data was collected at
30K resolution in MS mode. The width of the XIC was set to mimic single quadrupole
analysis (XIC = 0.7 Da) and high resolution mode (XIC = 10 mDa).
Figure 2: Isotope distribution of neuromedin U for the dominant charge states (Z=+3 and Z=+4). When the highlighted isotopes are extracted and combined, they represent 91% of
the peptide signal, thus improving the overall sensitivity of the system.
20
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
21
TECHNOLOGY SPOTLIGHT
TECHNOLOGY SPOTLIGHT
For Research Use Only. Not for use in diagnostic procedures.
Figure 4: Narrow-width extracted ion chromatogram (XIC) associated with dominant fragment masses of the illustrated peptides. The full scan mass spectrum can also be compared
to library spectrum to gain further confidence in the detection of the peptide.
Figure 3: Neuromedin U spiked into protein precipitated plasma. For each charge state, the top 4 isotopes were extracted and summed. The three charge states can also be combined
to further improve the sensitivity as little noise is captured in the process.
22
RUO-MKT-01-1583-A
DRUG METABOLISM
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www.absciex.com
DRUG METABOLISM
23
TECHNOLOGY SPOTLIGHT
TECHNOLOGY SPOTLIGHT
For Research Use Only. Not for use in diagnostic procedures.
Figure 4: Narrow-width extracted ion chromatogram (XIC) associated with dominant fragment masses of the illustrated peptides. The full scan mass spectrum can also be compared
to library spectrum to gain further confidence in the detection of the peptide.
Figure 3: Neuromedin U spiked into protein precipitated plasma. For each charge state, the top 4 isotopes were extracted and summed. The three charge states can also be combined
to further improve the sensitivity as little noise is captured in the process.
22
RUO-MKT-01-1583-A
DRUG METABOLISM
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DRUG METABOLISM
23
PCVG: A Powerful Algorithm for Automating
Comprehensive Xenobiotic Metabolite
Identification
TECHNOLOGY SPOTLIGHT
TECHNOLOGY SPOTLIGHT
For Research Use Only. Not for use in diagnostic procedures.
Laura Baker 1, Suma Ramagiri 2
1
Contract Technical Writer at AB SCIEX, Pittsburgh, PA, 2AB SCIEX, Concord, Canada
Key challenges of metabolite identification in complex
biological matrices
•
•
•
•
Overlooking low-level drug metabolites in complex biological matrices
such as bile, plasma, and tissue extracts
Incomplete metabolite information leading to repeated sample analysis
and decreased productivity
Non-definitive metabolite identification and characterization due to
inadequate MS/MS information
Figure 2: SWATH™ Acquisition sequentially collects MS/MS information for selected
mass windows (swaths) across a total mass range of interest. Sequential Q1 isolation
was stepped over mass range of interest (e.g., 25 Da or user defined). The high
speed of the TripleTOF® 5600+ System allows for full coverage of the selected mass
range in an LC time scale and for high-resolution XIC data for all fragment ions.
Multiple, non-integrated software platforms complicate data
processing, slowing metabolite ID and structure elucidation
Key benefits of SWATH™ Acquisition and PCVG algorithm
for metabolite ID
•
•
•
•
Comprehensive metabolite fingerprinting of irreplaceable experimental
samples using SWATH Acquisition. Having a complete array of spectra
(both MS and MS/MS scans) provides a digital archive of all analytes
for samples with restricted availability (e.g., pediatric studies, expensive
toxicological studies).
The ultimate safety net of 100% MS/MS coverage is realized by
capturing structural information for both predicted and unpredicted
metabolites, including low-level and genotoxic products.
MetabolitePilot™ Software is an all-in-one integrated software tool
that helps rapidly identify and confirm metabolites with structural
elucidation capabilities built-in without the need to switch between
multiple software tools.
Easy method development and retrospective data-mining
-- Requires no sample-specific method development
-- Creates a digital archive of all analytes, enabling retrospective
investigations without re-acquisition
Key features of SWATH Acquisition and PCVG algorithm
for metabolite identification
•
•
•
•
•
24
Selective MS/MS quantitation is achieved using single or multiple
product ions that are summed from multiple transitions during multiple
reaction monitoring (MRM) experiments.
PCVG-correlation of related peaks creates a less complex MS/MS
spectrum than traditional data-independent acquisition strategies.
Full retention of the isotopic pattern for each fragment due to a
wider Q1 selection is ideal for stable-label drug studies (14Cmetabolism studies) and 100% MS/MS coverage for low-level
metabolite/catabolite ID.
PCVG algorithm enables simplified interpretation and reduction of
data dimensionality for complex metabolite spectra generated using
data-independent acquisition.
PCVG algorithm is a fast, robust, and reliable approach for the
deconvolution of multi-component fragment ion spectra that is
applied within a research version of MetabolitePilot™ Software.
RUO-MKT-01-1583-A
Figure 1: SWATH™ Acquisition of all fragment ions for all precursors results in
complex data sets. The non-specific fragmentation data collection strategy generates
an unbiased record of all fragments of all precursors within one SWATH Acquisition
isolation window. Fragments that belong to the same precursor follow the
chromatographic profile of that precursor.
Drug metabolism studies have traditionally relied upon compoundspecific LC/MS/MS analyses to quantitate and identify metabolites
during the drug discovery and development process. Early stage
identification of all metabolites – including low-abundance
products – is not always possible, and biotransformation scientists
occasionally must backtrack, revisiting samples to unearth
information on previously-unidentified compounds. If MS/MS
spectra could be acquired upfront for all metabolites, both known
and unknown, during the drug discovery process, the chances of
overlooking an unpredicted metabolite would decrease, saving
time and satisfying regulatory requirements more quickly. Novel,
data-independent acquisition (DIA) strategies such as SWATH
Acquisition, now make non-targeted analyses a reality in complex
biological matrices and provide an overall snapshot of lowabundance, genotoxic, and major metabolites. Having complete
coverage creates a richer, more detailed picture, but wading
through the expanse of data – MS/MS spectra for every fragment
of every precursor ion – can be daunting and time-consuming.
Deconstructing this amassed data into interpretable results
requires a powerful algorithm called principal components variable
grouping (PCVG) that effectively filters unabridged MS/MS data to
extract comprehensive identification and quantitative information.
DRUG METABOLISM
www.absciex.com
Generating complete metabolite fragment ion data sets
using SWATH Acquisition
Recently, AB SCIEX scientists showed that PCVG algorithms could
rapidly identify and quantify drug metabolites formed in complex
biological matrices using DIA methods.1 In these studies, all sample
components were acquired in a single injection using SWATH
Acquisition, an innovative, non-targeted LC/MS/MS data collection
system on TripleTOF® 5600+ system, where an all-inclusive
fingerprint is generated from MS/MS scans of every parent ion in
the sample (Figure 1). SWATH Acquisition permits a full cluster of
ions within a wide Q1 mass window to travel concurrently into
the collision cell for fragmentation. Subsequent SWATH scans
conducted during the same injection sequentially collect fragment
ion information on incrementally increasing mass segments across
the total mass range of interest (Figure 2). The resulting composite
fragment ion data sets for each drug metabolite sample were laden
with thousands of MS/MS spectra, rich in metabolic information.
Additionally, a given MS/MS spectrum may be a combination of
spectra for two or more metabolites, convoluted in such a way that
interpretation of these raw, unprocessed spectra can be complex.
grouping (PCVG) into a research version of MetabolitePilot™
Software. PCVG reduces the dimensionality of complex data sets
by combining correlated variables into new representative groups
that are related to a particular peak in the LC/MS chromatogram,
delivering data that is easier to manipulate and understand. The
PCVG algorithm uses an unsupervised method to assign related
variables to groups, while also filtering out uncorrelated variables.
Grouped variables are replaced by a single representative prior
to additional data processing, reducing the data set for more
efficient interpretation. Deconvolution by the PCVG algorithm
proceeds in the following manner:
•
•
•
•
•
•
The pre-processed, aligned, multiplexed fragment ion spectra (i.e.,
SWATH data) undergo principal component analysis (PCA).
The variables are m/z values, the samples for the initial PCA are the raw,
non-specific fragmentation spectra, and the resulting groups are the
pure MS/MS spectra.
PCVG analyzes the PCA loadings values to find correlated variables
(fragment m/z values).
PCVG automates data reduction, filtering variables that do not correlate
with the target LC peak profile.
The smaller, deconvoluted data set facilitates spectral simplification,
aiding in data interpretation.
PCVG processing correlates signals across the entire mass range
examined, which, in turn, allows researchers to untangle complex
relationships from among peaks of interest and to link information on
isotopes, adducts, and fragments to related compounds.
PCVG filtering finds and correlates related metabolite peaks
PCVG deconvolution reduces and simplifies complex
multivariate data sets
Advances in LC/MS/MS DIA methods have produced fragment ion
data sets so vast that discovering the critical connections amongst
correlated data points is challenging without further data
processing. To simplify multivariate LC/MS data processing within
metabolite identification workflows, AB SCIEX scientists have
integrated a novel algorithm called principal component variable
www.absciex.com
Figure 3: TOF MS/MS spectra of nefazodone metabolites collected with IDA and
SWATH™ Acquisition methods. SWATH Acquisition with PCVG-filtering result in MS/
MS spectra that contain the full isotope pattern for fragment ions. Both background
subtract and PCVG-filtering strategies yielded accurate mass information enabling
more confident structure proposal. In the PCVG-filtered TOF MS/MS spectrum,
an additional minor peak (m/z 317) that corresponds to a direct substructure of
nefazodone was recovered from the raw data.
Motivated by the successful reduction of the dimensionality of
other LC/MS multivariate data sets obtained for proteomic2 and
drug metabolite discoveries, AB SCIEX scientists applied PCVG
to xenobiotic metabolite data generated by SWATH Acquisition
to derive a fingerprint of all parent-related compounds, creating
a more easily interpretable data set that equaled and – for some
analytes – surpassed the results obtained using informationdependent acquisition (IDA) methods. After deconvoluting
DRUG METABOLISM
25
PCVG: A Powerful Algorithm for Automating
Comprehensive Xenobiotic Metabolite
Identification
TECHNOLOGY SPOTLIGHT
TECHNOLOGY SPOTLIGHT
For Research Use Only. Not for use in diagnostic procedures.
Laura Baker 1, Suma Ramagiri 2
1
Contract Technical Writer at AB SCIEX, Pittsburgh, PA, 2AB SCIEX, Concord, Canada
Key challenges of metabolite identification in complex
biological matrices
•
•
•
•
Overlooking low-level drug metabolites in complex biological matrices
such as bile, plasma, and tissue extracts
Incomplete metabolite information leading to repeated sample analysis
and decreased productivity
Non-definitive metabolite identification and characterization due to
inadequate MS/MS information
Figure 2: SWATH™ Acquisition sequentially collects MS/MS information for selected
mass windows (swaths) across a total mass range of interest. Sequential Q1 isolation
was stepped over mass range of interest (e.g., 25 Da or user defined). The high
speed of the TripleTOF® 5600+ System allows for full coverage of the selected mass
range in an LC time scale and for high-resolution XIC data for all fragment ions.
Multiple, non-integrated software platforms complicate data
processing, slowing metabolite ID and structure elucidation
Key benefits of SWATH™ Acquisition and PCVG algorithm
for metabolite ID
•
•
•
•
Comprehensive metabolite fingerprinting of irreplaceable experimental
samples using SWATH Acquisition. Having a complete array of spectra
(both MS and MS/MS scans) provides a digital archive of all analytes
for samples with restricted availability (e.g., pediatric studies, expensive
toxicological studies).
The ultimate safety net of 100% MS/MS coverage is realized by
capturing structural information for both predicted and unpredicted
metabolites, including low-level and genotoxic products.
MetabolitePilot™ Software is an all-in-one integrated software tool
that helps rapidly identify and confirm metabolites with structural
elucidation capabilities built-in without the need to switch between
multiple software tools.
Easy method development and retrospective data-mining
-- Requires no sample-specific method development
-- Creates a digital archive of all analytes, enabling retrospective
investigations without re-acquisition
Key features of SWATH Acquisition and PCVG algorithm
for metabolite identification
•
•
•
•
•
24
Selective MS/MS quantitation is achieved using single or multiple
product ions that are summed from multiple transitions during multiple
reaction monitoring (MRM) experiments.
PCVG-correlation of related peaks creates a less complex MS/MS
spectrum than traditional data-independent acquisition strategies.
Full retention of the isotopic pattern for each fragment due to a
wider Q1 selection is ideal for stable-label drug studies (14Cmetabolism studies) and 100% MS/MS coverage for low-level
metabolite/catabolite ID.
PCVG algorithm enables simplified interpretation and reduction of
data dimensionality for complex metabolite spectra generated using
data-independent acquisition.
PCVG algorithm is a fast, robust, and reliable approach for the
deconvolution of multi-component fragment ion spectra that is
applied within a research version of MetabolitePilot™ Software.
RUO-MKT-01-1583-A
Figure 1: SWATH™ Acquisition of all fragment ions for all precursors results in
complex data sets. The non-specific fragmentation data collection strategy generates
an unbiased record of all fragments of all precursors within one SWATH Acquisition
isolation window. Fragments that belong to the same precursor follow the
chromatographic profile of that precursor.
Drug metabolism studies have traditionally relied upon compoundspecific LC/MS/MS analyses to quantitate and identify metabolites
during the drug discovery and development process. Early stage
identification of all metabolites – including low-abundance
products – is not always possible, and biotransformation scientists
occasionally must backtrack, revisiting samples to unearth
information on previously-unidentified compounds. If MS/MS
spectra could be acquired upfront for all metabolites, both known
and unknown, during the drug discovery process, the chances of
overlooking an unpredicted metabolite would decrease, saving
time and satisfying regulatory requirements more quickly. Novel,
data-independent acquisition (DIA) strategies such as SWATH
Acquisition, now make non-targeted analyses a reality in complex
biological matrices and provide an overall snapshot of lowabundance, genotoxic, and major metabolites. Having complete
coverage creates a richer, more detailed picture, but wading
through the expanse of data – MS/MS spectra for every fragment
of every precursor ion – can be daunting and time-consuming.
Deconstructing this amassed data into interpretable results
requires a powerful algorithm called principal components variable
grouping (PCVG) that effectively filters unabridged MS/MS data to
extract comprehensive identification and quantitative information.
DRUG METABOLISM
www.absciex.com
Generating complete metabolite fragment ion data sets
using SWATH Acquisition
Recently, AB SCIEX scientists showed that PCVG algorithms could
rapidly identify and quantify drug metabolites formed in complex
biological matrices using DIA methods.1 In these studies, all sample
components were acquired in a single injection using SWATH
Acquisition, an innovative, non-targeted LC/MS/MS data collection
system on TripleTOF® 5600+ system, where an all-inclusive
fingerprint is generated from MS/MS scans of every parent ion in
the sample (Figure 1). SWATH Acquisition permits a full cluster of
ions within a wide Q1 mass window to travel concurrently into
the collision cell for fragmentation. Subsequent SWATH scans
conducted during the same injection sequentially collect fragment
ion information on incrementally increasing mass segments across
the total mass range of interest (Figure 2). The resulting composite
fragment ion data sets for each drug metabolite sample were laden
with thousands of MS/MS spectra, rich in metabolic information.
Additionally, a given MS/MS spectrum may be a combination of
spectra for two or more metabolites, convoluted in such a way that
interpretation of these raw, unprocessed spectra can be complex.
grouping (PCVG) into a research version of MetabolitePilot™
Software. PCVG reduces the dimensionality of complex data sets
by combining correlated variables into new representative groups
that are related to a particular peak in the LC/MS chromatogram,
delivering data that is easier to manipulate and understand. The
PCVG algorithm uses an unsupervised method to assign related
variables to groups, while also filtering out uncorrelated variables.
Grouped variables are replaced by a single representative prior
to additional data processing, reducing the data set for more
efficient interpretation. Deconvolution by the PCVG algorithm
proceeds in the following manner:
•
•
•
•
•
•
The pre-processed, aligned, multiplexed fragment ion spectra (i.e.,
SWATH data) undergo principal component analysis (PCA).
The variables are m/z values, the samples for the initial PCA are the raw,
non-specific fragmentation spectra, and the resulting groups are the
pure MS/MS spectra.
PCVG analyzes the PCA loadings values to find correlated variables
(fragment m/z values).
PCVG automates data reduction, filtering variables that do not correlate
with the target LC peak profile.
The smaller, deconvoluted data set facilitates spectral simplification,
aiding in data interpretation.
PCVG processing correlates signals across the entire mass range
examined, which, in turn, allows researchers to untangle complex
relationships from among peaks of interest and to link information on
isotopes, adducts, and fragments to related compounds.
PCVG filtering finds and correlates related metabolite peaks
PCVG deconvolution reduces and simplifies complex
multivariate data sets
Advances in LC/MS/MS DIA methods have produced fragment ion
data sets so vast that discovering the critical connections amongst
correlated data points is challenging without further data
processing. To simplify multivariate LC/MS data processing within
metabolite identification workflows, AB SCIEX scientists have
integrated a novel algorithm called principal component variable
www.absciex.com
Figure 3: TOF MS/MS spectra of nefazodone metabolites collected with IDA and
SWATH™ Acquisition methods. SWATH Acquisition with PCVG-filtering result in MS/
MS spectra that contain the full isotope pattern for fragment ions. Both background
subtract and PCVG-filtering strategies yielded accurate mass information enabling
more confident structure proposal. In the PCVG-filtered TOF MS/MS spectrum,
an additional minor peak (m/z 317) that corresponds to a direct substructure of
nefazodone was recovered from the raw data.
Motivated by the successful reduction of the dimensionality of
other LC/MS multivariate data sets obtained for proteomic2 and
drug metabolite discoveries, AB SCIEX scientists applied PCVG
to xenobiotic metabolite data generated by SWATH Acquisition
to derive a fingerprint of all parent-related compounds, creating
a more easily interpretable data set that equaled and – for some
analytes – surpassed the results obtained using informationdependent acquisition (IDA) methods. After deconvoluting
DRUG METABOLISM
25
For Research Use Only. Not for use in diagnostic procedures.
TECHNOLOGY SPOTLIGHT
In combination, SWATH Acquisition and the PCVG algorithm
provide a powerful method for confident metabolic structure
assignment and offer many advantages when processing complex
MS/MS fragment ion data sets for quantitation and identification
of metabolites. The following benefits allow for improved
selectivity and specificity when pinpointing drug-related material:
•
•
Figure 4: MS/MS obtained using SWATH™ Acquision and retention of the full
isotopic pattern for a fragment from Biogen Idec drug candidate, BIIB021.5 MS/MS
data were deconvoluted (A) with PCVG filtering and (B) without PCVG-filtering.
•
•
the SWATH Acquisition data, researchers confirmed that the
PCVG-filtered MS/MS spectra included identical peaks at similar
intensities as those obtained by IDA.1 Shown here, SWATH
Acquisition of nefazodone metabolites resulted in full retention
of isotopic fragment ions and accurate mass information
(Figure 3). The PCVG filters maintained the fidelity of the raw
data and revealed a minor characteristic peak (m/z 317) that
correlated to the nefazodone structure. In similar experiments,
Biogen Idec scientist, Natasha Penner, used SWATH Acquisition to
systematically identify metabolites for a drug candidate (Table 1).
When comparing SWATH Acquisition data with results obtained
using more conventional IDA techniques, Dr. Penner observed an
increased number of PCVG-filtered metabolite peaks – 13 out
of the 13 known metabolites – with complete MS/MS coverage
when using SWATH Acquisition, surpassing results achieved with
generic TOF MS data-dependent acquisition. Additionally, each
fragment in the MS/MS data filtered by the PCVG algorithm,
retained the full isotopic pattern and compared favorably to
samples analyzed using traditional MS/MS approaches (Figure 4).
Taken together, these data validate and confirm the versatility
and accuracy of the PCVG method compared to well-established
IDA methods.
26
RUO-MKT-01-1583-A
•
The ultimate safety net is realized by capturing both predicted and
unpredicted metabolites. Having a complete array of spectra (both
MS and MS/MS scans) provides researchers a digital archive of all
analytes, so that data corresponding to unexpected products can be
retrospectively probed without having to re-acquire a sample.
Retention of full isotope pattern for each fragment is possible due
to the relatively wide swath window, which significantly aids in the
designation of metabolite structures and elemental composition during
metabolite discovery. This isotopic data along with 100% MS/MS
spectra provides sufficient structural information for the identification
and quantitation of low-abundance metabolites.
A less complex MS/MS spectrum is generated by MS/MSALL with SWATH
Acquisition than other DIA techniques. PCVG correlates related peaks,
and the relevant spectra make it easier to decide which parent ion goes
with which fragment, resulting in higher quality data even with complex
data from plasma or bile samples.
Easy method development allows researchers to focus on data analysis
instead of compound-specific methods. Because MS/MSALL with
SWATH Acquisition is a data-independent scan providing quantitative
information on all analytes, there is no need to create specialized, data
collection strategies for a particular drug candidate; this saves time and
unnecessary consumption of limited samples.
Multicomponent quantitation is possible with multiple fragment
ion transitions captured simultaneously during SWATH Acquisition
in a single injection. This adds an additional layer of confidence to
quantitative data by allowing multiple product ions to be summed.
In summary, the PCVG algorithm provides a fast, robust, and
reliable approach for deconvoluting non-specific fragmentation
data from drug metabolism studies obtained using SWATH
Acquisition. PCVG-filtering diminishes the complexity inherent
in large, multivariate data sets, while still creating a global
picture of xenobiotic drug metabolites and generating highlyinterpretable spectra for comprehensive metabolite identification
and quantitation. Used during the preliminary stages of the drug
discovery process, SWATH Acquisition coupled with the powerful
PCVG algorithm (incorporated into MetabolitePilot Software)
delivers complete metabolite coverage – even of minor products
– eliminating the possibility of a missed or underestimated
metabolite quantity, thereby streamlining the development of
new drug candidates and furthering the understanding of their
biotransformation pathways.
DRUG METABOLISM
www.absciex.com
Metabolite
Formula
RT (min)
(M+H)+
Generic TOF-MS
SWATH HS
Parent
C14H15ClN6O
3.89
319.1068
√ (MS/MS)
√ (MS/MS)
Oxidation-1
C14H15N6O2Cl
3.63
335.1016
√ (MS/MS)
√ (MS/MS)
Oxidation-2
C14H15N6O2Cl
3.72
335.1016
√ (NO MS/MS)
√ (MS/MS)
Oxidation-3
C14H15N6O2Cl
3.81
335.1016
√ (MS/MS)
√ (MS/MS)
Oxidation-4
2
C H N O Cl
4.11
335.1016
√ (NO MS/MS)
√ (MS/MS)
Dechlorination &
loss of CH2
C14H16N6O2
3.27
301.1405
X
√ (MS/MS)
Demethylation
C13H13N6OCl
3.45
305.0914
√ (MS/MS)
√ (MS/MS)
Internal hydrolysis
C14H17N6O2Cl
3.47
337.1174
√ (NO MS/MS)
√ (MS/MS)
Loss of
C5H2N5Cl+oxidation
C9H13NO2
0.99
168.1024
√ (NO MS/MS)
√ (MS/MS)
Loss of C9H11NO
C5H4N5Cl
0.98
170.0232
√ (MS/MS)
√ (MS/MS)
Di-oxidation
C14H15N6O3Cl
3.48
351.0967
√ (MS/MS)
√ (MS/MS)
Ketone formation
2
C H N O Cl
4.11
333.0861
x
√ (MS/MS)
Dechlorination, loss of
CH2 + internal hydrolysis
& di-oxidation
C13H16N6O4
3.43
321.087
x
√ (MS/MS)
Loss of Cl di-oxidation
C14H16N6O3
2.2
317.1354
x
√ (MS/MS)
Total metabolite coverage
9/13 (70%)
13/13 (100%)
Total MS / MS coverage
5/9 (55%)
13/13 (100%)
14
14
15
13
6
6
TECHNOLOGY SPOTLIGHT
Advantages of metabolite discovery with SWATH Acquisition
and PCVG-filtering
Table 1: Metabolite coverage for Biogen Idec drug candidate BIIB021 in complex biological matrix using generic TOF-MS IDA and SWATH™ Acquisition.5
References
1
Duchoslav E, Ivosev G, Shilov I, Ghobarah H, Burton L. “Automated metabolite identification
and profiling in non-specific fragmentation high-resolution accurate MS data.” Poster session
presented at: the 61st annual conference of the American Society for Mass Spectromtery; 2013
June 9-13; Minneapolis, MN.
2
Gillet LC, Navarro P, Tate S, Röst H, Slevsek N, Reiter L, Bonner R, Aebersold R. “Targeted data
extraction of the MS/MS spectra generated by data-independent acquisition: a new concept
for consistent and accurate proteome analysis.” Mol. Cell Proteomics. June 2012. DOI 10.1074/
mcp.0111.016717
3
Ivosev G., Burton L., Bonner R. “Dimensionality reduction and visualization in principal
component analysis.” Anal. Chem. July 2008. 80: 4933-4944.
4
Hopfgartner, G. “High-resolution mass spectrometry for integrated qualitative and quantitative
analysis of pharmaceuticals in biological matrices.” Anal Bioanal Chem, March 2012. 402(8):
2587-96.
5
Penner N. “High throughput metabolite ID: Are we there yet?” AB SCIEX Mass Spec Webinar
Series. July 2013. Retrieved at: http://www.absciex.com/events/webinars/high-throughput
metabolite-id-are-we-there-yet
www.absciex.com
DRUG METABOLISM
27
For Research Use Only. Not for use in diagnostic procedures.
TECHNOLOGY SPOTLIGHT
In combination, SWATH Acquisition and the PCVG algorithm
provide a powerful method for confident metabolic structure
assignment and offer many advantages when processing complex
MS/MS fragment ion data sets for quantitation and identification
of metabolites. The following benefits allow for improved
selectivity and specificity when pinpointing drug-related material:
•
•
Figure 4: MS/MS obtained using SWATH™ Acquision and retention of the full
isotopic pattern for a fragment from Biogen Idec drug candidate, BIIB021.5 MS/MS
data were deconvoluted (A) with PCVG filtering and (B) without PCVG-filtering.
•
•
the SWATH Acquisition data, researchers confirmed that the
PCVG-filtered MS/MS spectra included identical peaks at similar
intensities as those obtained by IDA.1 Shown here, SWATH
Acquisition of nefazodone metabolites resulted in full retention
of isotopic fragment ions and accurate mass information
(Figure 3). The PCVG filters maintained the fidelity of the raw
data and revealed a minor characteristic peak (m/z 317) that
correlated to the nefazodone structure. In similar experiments,
Biogen Idec scientist, Natasha Penner, used SWATH Acquisition to
systematically identify metabolites for a drug candidate (Table 1).
When comparing SWATH Acquisition data with results obtained
using more conventional IDA techniques, Dr. Penner observed an
increased number of PCVG-filtered metabolite peaks – 13 out
of the 13 known metabolites – with complete MS/MS coverage
when using SWATH Acquisition, surpassing results achieved with
generic TOF MS data-dependent acquisition. Additionally, each
fragment in the MS/MS data filtered by the PCVG algorithm,
retained the full isotopic pattern and compared favorably to
samples analyzed using traditional MS/MS approaches (Figure 4).
Taken together, these data validate and confirm the versatility
and accuracy of the PCVG method compared to well-established
IDA methods.
26
RUO-MKT-01-1583-A
•
The ultimate safety net is realized by capturing both predicted and
unpredicted metabolites. Having a complete array of spectra (both
MS and MS/MS scans) provides researchers a digital archive of all
analytes, so that data corresponding to unexpected products can be
retrospectively probed without having to re-acquire a sample.
Retention of full isotope pattern for each fragment is possible due
to the relatively wide swath window, which significantly aids in the
designation of metabolite structures and elemental composition during
metabolite discovery. This isotopic data along with 100% MS/MS
spectra provides sufficient structural information for the identification
and quantitation of low-abundance metabolites.
A less complex MS/MS spectrum is generated by MS/MSALL with SWATH
Acquisition than other DIA techniques. PCVG correlates related peaks,
and the relevant spectra make it easier to decide which parent ion goes
with which fragment, resulting in higher quality data even with complex
data from plasma or bile samples.
Easy method development allows researchers to focus on data analysis
instead of compound-specific methods. Because MS/MSALL with
SWATH Acquisition is a data-independent scan providing quantitative
information on all analytes, there is no need to create specialized, data
collection strategies for a particular drug candidate; this saves time and
unnecessary consumption of limited samples.
Multicomponent quantitation is possible with multiple fragment
ion transitions captured simultaneously during SWATH Acquisition
in a single injection. This adds an additional layer of confidence to
quantitative data by allowing multiple product ions to be summed.
In summary, the PCVG algorithm provides a fast, robust, and
reliable approach for deconvoluting non-specific fragmentation
data from drug metabolism studies obtained using SWATH
Acquisition. PCVG-filtering diminishes the complexity inherent
in large, multivariate data sets, while still creating a global
picture of xenobiotic drug metabolites and generating highlyinterpretable spectra for comprehensive metabolite identification
and quantitation. Used during the preliminary stages of the drug
discovery process, SWATH Acquisition coupled with the powerful
PCVG algorithm (incorporated into MetabolitePilot Software)
delivers complete metabolite coverage – even of minor products
– eliminating the possibility of a missed or underestimated
metabolite quantity, thereby streamlining the development of
new drug candidates and furthering the understanding of their
biotransformation pathways.
DRUG METABOLISM
www.absciex.com
Metabolite
Formula
RT (min)
(M+H)+
Generic TOF-MS
SWATH HS
Parent
C14H15ClN6O
3.89
319.1068
√ (MS/MS)
√ (MS/MS)
Oxidation-1
C14H15N6O2Cl
3.63
335.1016
√ (MS/MS)
√ (MS/MS)
Oxidation-2
C14H15N6O2Cl
3.72
335.1016
√ (NO MS/MS)
√ (MS/MS)
Oxidation-3
C14H15N6O2Cl
3.81
335.1016
√ (MS/MS)
√ (MS/MS)
Oxidation-4
2
C H N O Cl
4.11
335.1016
√ (NO MS/MS)
√ (MS/MS)
Dechlorination &
loss of CH2
C14H16N6O2
3.27
301.1405
X
√ (MS/MS)
Demethylation
C13H13N6OCl
3.45
305.0914
√ (MS/MS)
√ (MS/MS)
Internal hydrolysis
C14H17N6O2Cl
3.47
337.1174
√ (NO MS/MS)
√ (MS/MS)
Loss of
C5H2N5Cl+oxidation
C9H13NO2
0.99
168.1024
√ (NO MS/MS)
√ (MS/MS)
Loss of C9H11NO
C5H4N5Cl
0.98
170.0232
√ (MS/MS)
√ (MS/MS)
Di-oxidation
C14H15N6O3Cl
3.48
351.0967
√ (MS/MS)
√ (MS/MS)
Ketone formation
2
C H N O Cl
4.11
333.0861
x
√ (MS/MS)
Dechlorination, loss of
CH2 + internal hydrolysis
& di-oxidation
C13H16N6O4
3.43
321.087
x
√ (MS/MS)
Loss of Cl di-oxidation
C14H16N6O3
2.2
317.1354
x
√ (MS/MS)
Total metabolite coverage
9/13 (70%)
13/13 (100%)
Total MS / MS coverage
5/9 (55%)
13/13 (100%)
14
14
15
13
6
6
TECHNOLOGY SPOTLIGHT
Advantages of metabolite discovery with SWATH Acquisition
and PCVG-filtering
Table 1: Metabolite coverage for Biogen Idec drug candidate BIIB021 in complex biological matrix using generic TOF-MS IDA and SWATH™ Acquisition.5
References
1
Duchoslav E, Ivosev G, Shilov I, Ghobarah H, Burton L. “Automated metabolite identification
and profiling in non-specific fragmentation high-resolution accurate MS data.” Poster session
presented at: the 61st annual conference of the American Society for Mass Spectromtery; 2013
June 9-13; Minneapolis, MN.
2
Gillet LC, Navarro P, Tate S, Röst H, Slevsek N, Reiter L, Bonner R, Aebersold R. “Targeted data
extraction of the MS/MS spectra generated by data-independent acquisition: a new concept
for consistent and accurate proteome analysis.” Mol. Cell Proteomics. June 2012. DOI 10.1074/
mcp.0111.016717
3
Ivosev G., Burton L., Bonner R. “Dimensionality reduction and visualization in principal
component analysis.” Anal. Chem. July 2008. 80: 4933-4944.
4
Hopfgartner, G. “High-resolution mass spectrometry for integrated qualitative and quantitative
analysis of pharmaceuticals in biological matrices.” Anal Bioanal Chem, March 2012. 402(8):
2587-96.
5
Penner N. “High throughput metabolite ID: Are we there yet?” AB SCIEX Mass Spec Webinar
Series. July 2013. Retrieved at: http://www.absciex.com/events/webinars/high-throughput
metabolite-id-are-we-there-yet
www.absciex.com
DRUG METABOLISM
27
DiscoveryQuant™ Software 2.0: The Definitive
Solution for LC/MS/MS Early-ADME Workflows
Key scientific challenges of early-ADME workflows
•
•
•
Overview
Productivity is one of the top, if not the main,
driver in drug discovery.
Sample Pooling – To increase productivity, multiple drugs are
often cassette-dosed.
Data Sharing – Global companies need to share information globally.
Key benefits of the DiscoveryQuant™ Software
early-ADME workflows
•
•
•
Speed – The software can both optimize the system and prosystems to
provide the high levels of sensitivity for MRM analysis.
Rapid data review – The ability to quickly visualize the data
from a microtiter plate speeds data review.
Time-saving templates – Pre-set methods allow users to spend less time
setting up runs and more time acquiring data.
Key features of DiscoveryQuant Software
early-ADME workflows
•
•
•
•
Cassetting wizard – A pre-set method for pooling samples makes setup
fast and simple.
Multiply injected assay support – Maximum autosampler speed
(Shimadzu, CTC, Agilent, or Acuity) is allowed while continually
acquiring data for increased throughput.
LIMS connectivity – Improved laboratory informatics are enabled
through easily configurable output column mapping taking full
advantage of established enterprise-wide systems.
Global database support – Compounds optimized in one lab can
be used at another without re-optimizing, improving efficiency and
productivity across organizations.
Today’s high-performance discovery lab performs many different
assays on an increasing number of novel compounds. To improve
the discovery pipeline, drug discovery labs must gather more
information on a greater number of new chemical entities. Platebased technologies, shared conditions information, and pooling
strategies are often used to increase throughput in the lab. New
software tools can also greatly improve the situation with process
automation, information sharing, and functionality monitoring.
DiscoveryQuant™ Software 2.0 has been designed specifically
for early-ADME workflows in a drug discovery environment.
Using a simple and elegant workflow, DiscoveryQuant
Software 2.0 is a very powerful and comprehensive software
solution that streamlines the drug discovery process by providing
better efficiency and productivity.
With the Optimize and Analyze modules, researchers can work
in parallel on multiple MS systems. The Optimize module will
automatically determine the optimal ion path parameters (DP
and CE) for maximum quantitative sensitivity for test compounds
and then populate a database with this information. The Analyze
module can also access MS methods from the database for use in
standard assays. Implementing a shared database accessed by labs
around the world, different teams can seamlessly collaborate with
distant colleagues, working together to avoid duplicating effort.
Figure 1: The Optimize module in DiscoveryQuant Software maximizes compound-dependent parameters that are populated into a database. The Analyze module allows
users to create quantitative methods for their analytes based on the stored values in the DiscoveryQuant Software database. Analyze is also used to create batches, run studies,
integrate data, and generate final reports.
28
RUO-MKT-01-1583-A
EARLY DISCOVERY
EARLY DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
DRUG METABOLISM
www.absciex.com
Figure 2: The intuitive software interface makes plate review simple and fast.
Intuitive interface
The graphical user interface has been designed to be intuitive and
easy-to-navigate. The main workflows are set out on consecutive
tabs at the top of the page that are completed consecutively to
optimize method development. Data can be easily imported to
avoid manual re-entry, and the information can be saved and
recalled as needed.
Concise plate review
The results of an entire plate can be seen graphically. The colorcoded display gives immediate and clear visualization of results
and quick access to individual problem samples with a single click.
Express cassetting wizard
Compounds for dosing can be grouped directly within the
software to facilitate straightforward analytical setup. The
intelligently designed DiscoveryQuant Software 2.0 will warn
of any conflicts in polarity or interfering parent or fragement
masses. Because the positive and negative ion experiments are
run separately, it takes twice as long to obtain complete data
potentially complicating data analysis with LC retention time
Figure 4: Plate templates make large-batch sample entry efficient and easy.
www.absciex.com
Figure 3: Peak Review allows you to quickly check injection and integration in one
interactive window.
shifts between samples. Furthermore, greater sample amounts
are required for a two-injection workflow. Use of the express
cassetting wizard will bypass these issues with assays that require
polarity switching.
Support for multiple autosampler platforms
Maximize costly MS instrument time by continually acquiring
over multiple injections, improving throughput and productivity.
When the run is done, the software deconvolutes the analysis
into individual compound results, reducing instrument calibration
and delaying overhead during device communication and
synchronization.
Rapid review capability
Multiple concentrations of a single compound can be reviewed
across many injections. The software stitches runs together
automatically to give a clear and concise picture of test results.
Figure 5: The calibration curve window displays the calibration data and the linear
regression for each MRM transition.
DRUG METABOLISM
29
DiscoveryQuant™ Software 2.0: The Definitive
Solution for LC/MS/MS Early-ADME Workflows
Key scientific challenges of early-ADME workflows
•
•
•
Overview
Productivity is one of the top, if not the main,
driver in drug discovery.
Sample Pooling – To increase productivity, multiple drugs are
often cassette-dosed.
Data Sharing – Global companies need to share information globally.
Key benefits of the DiscoveryQuant™ Software
early-ADME workflows
•
•
•
Speed – The software can both optimize the system and prosystems to
provide the high levels of sensitivity for MRM analysis.
Rapid data review – The ability to quickly visualize the data
from a microtiter plate speeds data review.
Time-saving templates – Pre-set methods allow users to spend less time
setting up runs and more time acquiring data.
Key features of DiscoveryQuant Software
early-ADME workflows
•
•
•
•
Cassetting wizard – A pre-set method for pooling samples makes setup
fast and simple.
Multiply injected assay support – Maximum autosampler speed
(Shimadzu, CTC, Agilent, or Acuity) is allowed while continually
acquiring data for increased throughput.
LIMS connectivity – Improved laboratory informatics are enabled
through easily configurable output column mapping taking full
advantage of established enterprise-wide systems.
Global database support – Compounds optimized in one lab can
be used at another without re-optimizing, improving efficiency and
productivity across organizations.
Today’s high-performance discovery lab performs many different
assays on an increasing number of novel compounds. To improve
the discovery pipeline, drug discovery labs must gather more
information on a greater number of new chemical entities. Platebased technologies, shared conditions information, and pooling
strategies are often used to increase throughput in the lab. New
software tools can also greatly improve the situation with process
automation, information sharing, and functionality monitoring.
DiscoveryQuant™ Software 2.0 has been designed specifically
for early-ADME workflows in a drug discovery environment.
Using a simple and elegant workflow, DiscoveryQuant
Software 2.0 is a very powerful and comprehensive software
solution that streamlines the drug discovery process by providing
better efficiency and productivity.
With the Optimize and Analyze modules, researchers can work
in parallel on multiple MS systems. The Optimize module will
automatically determine the optimal ion path parameters (DP
and CE) for maximum quantitative sensitivity for test compounds
and then populate a database with this information. The Analyze
module can also access MS methods from the database for use in
standard assays. Implementing a shared database accessed by labs
around the world, different teams can seamlessly collaborate with
distant colleagues, working together to avoid duplicating effort.
Figure 1: The Optimize module in DiscoveryQuant Software maximizes compound-dependent parameters that are populated into a database. The Analyze module allows
users to create quantitative methods for their analytes based on the stored values in the DiscoveryQuant Software database. Analyze is also used to create batches, run studies,
integrate data, and generate final reports.
28
RUO-MKT-01-1583-A
EARLY DISCOVERY
EARLY DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
DRUG METABOLISM
www.absciex.com
Figure 2: The intuitive software interface makes plate review simple and fast.
Intuitive interface
The graphical user interface has been designed to be intuitive and
easy-to-navigate. The main workflows are set out on consecutive
tabs at the top of the page that are completed consecutively to
optimize method development. Data can be easily imported to
avoid manual re-entry, and the information can be saved and
recalled as needed.
Concise plate review
The results of an entire plate can be seen graphically. The colorcoded display gives immediate and clear visualization of results
and quick access to individual problem samples with a single click.
Express cassetting wizard
Compounds for dosing can be grouped directly within the
software to facilitate straightforward analytical setup. The
intelligently designed DiscoveryQuant Software 2.0 will warn
of any conflicts in polarity or interfering parent or fragement
masses. Because the positive and negative ion experiments are
run separately, it takes twice as long to obtain complete data
potentially complicating data analysis with LC retention time
Figure 4: Plate templates make large-batch sample entry efficient and easy.
www.absciex.com
Figure 3: Peak Review allows you to quickly check injection and integration in one
interactive window.
shifts between samples. Furthermore, greater sample amounts
are required for a two-injection workflow. Use of the express
cassetting wizard will bypass these issues with assays that require
polarity switching.
Support for multiple autosampler platforms
Maximize costly MS instrument time by continually acquiring
over multiple injections, improving throughput and productivity.
When the run is done, the software deconvolutes the analysis
into individual compound results, reducing instrument calibration
and delaying overhead during device communication and
synchronization.
Rapid review capability
Multiple concentrations of a single compound can be reviewed
across many injections. The software stitches runs together
automatically to give a clear and concise picture of test results.
Figure 5: The calibration curve window displays the calibration data and the linear
regression for each MRM transition.
DRUG METABOLISM
29
Time-saving templates
Highlights
Plate layouts can be chosen from pre-defined templates that
are user-built and then shared. Less time can be spent entering
sample locations by entering a few samples for a new plate and
then clicking “Propagate Groups” to auto-fill the remaining
sample names based on the currently defined pattern. With
multiple plates of samples to run, easy sample entry reduces the
tedium typically associated with large-batch analysis.
•
Quick-view calibration curves
•
•
•
EARLY DISCOVERY
EARLY DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
The cassetting wizard makes setup of pooled samples fast and simple.
Support for multiple autosampler platforms allows maximum injection
speed (Shimadzu, CTC, Agilent, or Acuity) while continually acquiring
data for increased throughput.
LIMS connectivity enabled through easily configurable output column
mapping takes full advantage of established, enterprise-wide systems.
Global database support allows compounds optimized in one lab to
be used at another without re-optimization, improving efficiency and
productivity across organizations.
Data export to an external package for regression is eliminated
with quick-view calibration curves incorporated within the
software package. Regression analysis can be conducted
immediately after data acquisition, and graphical results can be
viewed internally.
Custom column mapping
The order and name of column headers can be user-defined,
outputting results appear in a .CSV output. This allows files to
legacy LIMS systems for a final report, without any intervention or
manual modification of the results.
Global database setup
The global database support in DiscoveryQuant Software 2.0
can be used to store and share mass spectrometry methods.
The size of research laboratories can be leveraged to create
greater sample capacity and productivity by drastically reducing
redundant work. When enabled, the software will automatically
look for a method in the database before acquiring data
to build a new one. This allows for methods from distant
colleagues around the globe to be shared to keep even the
most remote labs running consistently and efficiently.
Summary
DiscoveryQuant Software 2.0 has been designed by experts in
early-ADME drug discovery who understand the intricacies and
needs of this area. This software has been carefully designed to
deliver high-throughput analyses without compromising results –
so that efficiency is not sacrificed over quality.
This software allows researchers to easily stay connected
with colleagues in other ADME research areas throughout an
organization. Methods can be stored in the database and shared
globally. In an environment where efficiency is vital, duplication of
efforts can be avoided to get the most out of mass spectrometry
analysis during ADME research.
With DiscoveryQuant Software 2.0, the LC/MS/MS early-ADME
workflow is completely covered – no more partial solutions or
manual data transport to other systems. DiscoveryQuant Software
provides a total solution from beginning to end and everywhere in
between. Early-ADME workflows have never seen anything like this.
For a free 90-day trial of this software, visit:
www.absciex.com/discoveryquant
30
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
31
Time-saving templates
Highlights
Plate layouts can be chosen from pre-defined templates that
are user-built and then shared. Less time can be spent entering
sample locations by entering a few samples for a new plate and
then clicking “Propagate Groups” to auto-fill the remaining
sample names based on the currently defined pattern. With
multiple plates of samples to run, easy sample entry reduces the
tedium typically associated with large-batch analysis.
•
Quick-view calibration curves
•
•
•
EARLY DISCOVERY
EARLY DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
The cassetting wizard makes setup of pooled samples fast and simple.
Support for multiple autosampler platforms allows maximum injection
speed (Shimadzu, CTC, Agilent, or Acuity) while continually acquiring
data for increased throughput.
LIMS connectivity enabled through easily configurable output column
mapping takes full advantage of established, enterprise-wide systems.
Global database support allows compounds optimized in one lab to
be used at another without re-optimization, improving efficiency and
productivity across organizations.
Data export to an external package for regression is eliminated
with quick-view calibration curves incorporated within the
software package. Regression analysis can be conducted
immediately after data acquisition, and graphical results can be
viewed internally.
Custom column mapping
The order and name of column headers can be user-defined,
outputting results appear in a .CSV output. This allows files to
legacy LIMS systems for a final report, without any intervention or
manual modification of the results.
Global database setup
The global database support in DiscoveryQuant Software 2.0
can be used to store and share mass spectrometry methods.
The size of research laboratories can be leveraged to create
greater sample capacity and productivity by drastically reducing
redundant work. When enabled, the software will automatically
look for a method in the database before acquiring data
to build a new one. This allows for methods from distant
colleagues around the globe to be shared to keep even the
most remote labs running consistently and efficiently.
Summary
DiscoveryQuant Software 2.0 has been designed by experts in
early-ADME drug discovery who understand the intricacies and
needs of this area. This software has been carefully designed to
deliver high-throughput analyses without compromising results –
so that efficiency is not sacrificed over quality.
This software allows researchers to easily stay connected
with colleagues in other ADME research areas throughout an
organization. Methods can be stored in the database and shared
globally. In an environment where efficiency is vital, duplication of
efforts can be avoided to get the most out of mass spectrometry
analysis during ADME research.
With DiscoveryQuant Software 2.0, the LC/MS/MS early-ADME
workflow is completely covered – no more partial solutions or
manual data transport to other systems. DiscoveryQuant Software
provides a total solution from beginning to end and everywhere in
between. Early-ADME workflows have never seen anything like this.
For a free 90-day trial of this software, visit:
www.absciex.com/discoveryquant
30
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
31
Confirmation of In Vitro Nefazodone
Metabolites Using the Superior Fragmentation
of the QTRAP® 5500 LC/MS/MS System
Claire Bramwell-German, Elliott Jones, and Daniel Lebre
AB SCIEX, Redwood City, California and Concord, Ontario, Canada
Figure 1: Predictive multiple reaction monitoring (pMRM) example. This shows the
pMRM transition prediction for a transformation that is a combination of oxidation
and demethylation. In this case LightSight® Software 2.1 creates four MRMs which
will cover any fragment/transformation possibility. Since the MRM method has a
better duty cycle than any other MS method, sensitivity and selectivity are very
important in this mode.
Key scientific challenges of metabolite ID
•
•
•
Productivity – Increasing productivity in drug discovery and
development continues to be a primary goal in pharmaceutical
research.
Sensitivity – The ability to detect metabolites at physiologically relevant
concentrations is critical to drug development, and early detection
minimizes development costs for drugs that shouldn’t progress to the
development stage.
Introduction
Metabolite profiling plays a critical role in the discovery and
developmental level of analysis for lead drug candidates.
Many aspects of drug activity are affected by metabolism
(pharmacokinetics, bioavailability, drug distribution, toxicity,
and adverse drug reactions). The enhanced scan rate (4-fold for
quadrupole and 5-fold for LIT modes) and sensitivity increases of
(8- to 10-fold response in quadrupole mode and 10- to 100-fold
in LIT mode) allow for a number of unique experiments with
unprecedented sensitivity.
Ease of use – Most metabolite ID workflows require a skilled
operator to acquire and process the data, often using multiple
software packages.
Key benefits of the QTRAP® 5500 Systems and LightSight®
Software 2.1 for metabolite ID
•
•
•
Comprehensive software package – LightSight Software can be
used to build compound-specific methods, acquire the data, and
then process it.
Selective and sensitive scans – The QTRAP systems have
selective and sensitive scan modes that can be combined to
find the maximum number of metabolites in the minimal
number of injections.
Increased throughput – The faster speed of this instrument makes
it UHPLC compatible, providing greater sample throughput.
Key features of the QTRAP 5500 systems and LightSight
Software 2.1 for metabolite ID
•
•
•
Unique capabilities – With combinable quadrupole and ion
trap scan modes available in one instrument coupled with advanced
technologies, rapid, thorough metabolite ID is easily achievable.
Speed – Hardware improvements provide a system that is
UHPLC-compatible.
One software package – With the built-in ability to both acquire
and process data, LightSight Software makes the work of the
biotransformation chemist much easier.
Overview
Recently, nefazodone (an antidepressant) has been targeted
for metabolite studies due to the quantity and diversity of
biotransformations resulting from CYP450 enzymatic activity.
Several scientific publications have evaluated different workflows
and mass spectrometry platforms for this specific application.1-3
Herein, we report an effective and sensitive method to identify
nefazodone metabolites using the new QTRAP 5500 LC/MS/MS
system. The system’s quadrupole and linear ion trap sensitivity
and scan speed have been significantly improved, allowing for
highly effective detection and confirmation of in vitro and in
vivo metabolites. The faster scan rates are a perfect match for
small particle, high-pressure LC workflows for better separation
and throughput. Using a predictive multiple reaction monitoring
(pMRM) approach and a rapid, 10-minute chromatographic
analysis, a significant number of metabolites were found and
confirmed in a single injection – more than any other MS
approach found in previous literature references. This level of
identification in a rapid chromatographic analysis demonstrates
the abilities of the QTRAP 5500 system for high throughput and
a detailed metabolite analysis, with a depth of detection not
possible when using other MS technologies. Furthermore, the
improved sensitivity in the linear ion trap (LIT) mode allows
for fine structural confirmation, since a greater number of
diagnostic low-level fragments can be detected compared to
less sensitive accurate mass systems.
The most effective scan mode for first pass metabolic screening
on the QTRAP 5500 system is pMRM. Figure 1 shows an example
of pMRM detection in which a comprehensive list of possible
biotransformations along with parent drug fragment pattern
are used to create a range of theoretically derived MRMs to
cover all possible metabolite transitions. The only type of
fragmentation pattern where pMRM might miss a metabolite
is N-dealkylation and other exotic breaks in the drug core
structure. In this case, a second injection using a dual precursor
and neutral loss survey based on the parent drug’s fragmentation
pattern can be employed to pick up any overlooked metabolites.
This second injection would not be required if the molecule
is unlikely to undergo an N-dealkylation transformation. The
strength of the QTRAP 5500 system is that a cycle of ~300
pMRMs or a dual precursor and neutral loss survey with four
dependent ion trap MS/MS can be done approximately 4-fold
faster than on the current 3200 and 4000 QTRAP systems.
For example, a dual scan experiment for both a precursor and
neutral loss, with four enhanced product ion (EPI) scans running
at the fastest scan rate possible on the 4000 QTRAP system
could take ~7.5 sec. Because this cycle rate is too slow for most
chromatographic peaks, only a single survey and one or two EPI
scans could be done within an LC/MS time frame. However, as the
QTRAP 5500 system can scan 4- to 5-fold faster, this same cycle
would be reduced to around ~2 sec. A 2 sec cycle rate will allow
for at least three data points to be acquired over even a very sharp
6 sec chromatographic peak. The combined precursor and neutral
loss experiment is also particularly effective because few metabolic
changes would affect both the neutral and charged parts of the
molecule, allowing for broad metabolite detection. In the case of
many co-eluting metabolites, a greater number of dependent EPI
scans is possible based on the 5-fold faster LIT scan rate, which
will also allow for more MS/MS spectra to be acquired in a single
injection. Thus the dual scan survey with four EPI scans can allow
for greater metabolite detection without compromising scan rate.
(Dynamic Background Subtraction (DBS) is another way to ensure
efficient ion selection during an automated experiment and
doesn’t require more than one dependent EPI scan, significantly
reducing the cycle time.) This optimal mode is only possible on a
QTRAP 5500 system. Similarly to the dual survey mode, pMRM
experiments also benefit from a 4-fold reduction in scan rate
compared to the 4000 QTRAP system in much the same way.
EARLY DISCOVERY
EARLY DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Figure 2: Sensitivity gains on the QTRAP® 5500 LC/MS/MS System over the 4000 QTRAP® System. Three metabolites with corresponding linear ion trap (LIT) MS/MS spectra are
compared for the QTRAP 5500 system (left panels) and the 4000 QTRAP system (right panels.) In each case, a significant increase in the number of interpretable fragments is
obtained by the QTRAP 5500 system.
32
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
33
Confirmation of In Vitro Nefazodone
Metabolites Using the Superior Fragmentation
of the QTRAP® 5500 LC/MS/MS System
Claire Bramwell-German, Elliott Jones, and Daniel Lebre
AB SCIEX, Redwood City, California and Concord, Ontario, Canada
Figure 1: Predictive multiple reaction monitoring (pMRM) example. This shows the
pMRM transition prediction for a transformation that is a combination of oxidation
and demethylation. In this case LightSight® Software 2.1 creates four MRMs which
will cover any fragment/transformation possibility. Since the MRM method has a
better duty cycle than any other MS method, sensitivity and selectivity are very
important in this mode.
Key scientific challenges of metabolite ID
•
•
•
Productivity – Increasing productivity in drug discovery and
development continues to be a primary goal in pharmaceutical
research.
Sensitivity – The ability to detect metabolites at physiologically relevant
concentrations is critical to drug development, and early detection
minimizes development costs for drugs that shouldn’t progress to the
development stage.
Introduction
Metabolite profiling plays a critical role in the discovery and
developmental level of analysis for lead drug candidates.
Many aspects of drug activity are affected by metabolism
(pharmacokinetics, bioavailability, drug distribution, toxicity,
and adverse drug reactions). The enhanced scan rate (4-fold for
quadrupole and 5-fold for LIT modes) and sensitivity increases of
(8- to 10-fold response in quadrupole mode and 10- to 100-fold
in LIT mode) allow for a number of unique experiments with
unprecedented sensitivity.
Ease of use – Most metabolite ID workflows require a skilled
operator to acquire and process the data, often using multiple
software packages.
Key benefits of the QTRAP® 5500 Systems and LightSight®
Software 2.1 for metabolite ID
•
•
•
Comprehensive software package – LightSight Software can be
used to build compound-specific methods, acquire the data, and
then process it.
Selective and sensitive scans – The QTRAP systems have
selective and sensitive scan modes that can be combined to
find the maximum number of metabolites in the minimal
number of injections.
Increased throughput – The faster speed of this instrument makes
it UHPLC compatible, providing greater sample throughput.
Key features of the QTRAP 5500 systems and LightSight
Software 2.1 for metabolite ID
•
•
•
Unique capabilities – With combinable quadrupole and ion
trap scan modes available in one instrument coupled with advanced
technologies, rapid, thorough metabolite ID is easily achievable.
Speed – Hardware improvements provide a system that is
UHPLC-compatible.
One software package – With the built-in ability to both acquire
and process data, LightSight Software makes the work of the
biotransformation chemist much easier.
Overview
Recently, nefazodone (an antidepressant) has been targeted
for metabolite studies due to the quantity and diversity of
biotransformations resulting from CYP450 enzymatic activity.
Several scientific publications have evaluated different workflows
and mass spectrometry platforms for this specific application.1-3
Herein, we report an effective and sensitive method to identify
nefazodone metabolites using the new QTRAP 5500 LC/MS/MS
system. The system’s quadrupole and linear ion trap sensitivity
and scan speed have been significantly improved, allowing for
highly effective detection and confirmation of in vitro and in
vivo metabolites. The faster scan rates are a perfect match for
small particle, high-pressure LC workflows for better separation
and throughput. Using a predictive multiple reaction monitoring
(pMRM) approach and a rapid, 10-minute chromatographic
analysis, a significant number of metabolites were found and
confirmed in a single injection – more than any other MS
approach found in previous literature references. This level of
identification in a rapid chromatographic analysis demonstrates
the abilities of the QTRAP 5500 system for high throughput and
a detailed metabolite analysis, with a depth of detection not
possible when using other MS technologies. Furthermore, the
improved sensitivity in the linear ion trap (LIT) mode allows
for fine structural confirmation, since a greater number of
diagnostic low-level fragments can be detected compared to
less sensitive accurate mass systems.
The most effective scan mode for first pass metabolic screening
on the QTRAP 5500 system is pMRM. Figure 1 shows an example
of pMRM detection in which a comprehensive list of possible
biotransformations along with parent drug fragment pattern
are used to create a range of theoretically derived MRMs to
cover all possible metabolite transitions. The only type of
fragmentation pattern where pMRM might miss a metabolite
is N-dealkylation and other exotic breaks in the drug core
structure. In this case, a second injection using a dual precursor
and neutral loss survey based on the parent drug’s fragmentation
pattern can be employed to pick up any overlooked metabolites.
This second injection would not be required if the molecule
is unlikely to undergo an N-dealkylation transformation. The
strength of the QTRAP 5500 system is that a cycle of ~300
pMRMs or a dual precursor and neutral loss survey with four
dependent ion trap MS/MS can be done approximately 4-fold
faster than on the current 3200 and 4000 QTRAP systems.
For example, a dual scan experiment for both a precursor and
neutral loss, with four enhanced product ion (EPI) scans running
at the fastest scan rate possible on the 4000 QTRAP system
could take ~7.5 sec. Because this cycle rate is too slow for most
chromatographic peaks, only a single survey and one or two EPI
scans could be done within an LC/MS time frame. However, as the
QTRAP 5500 system can scan 4- to 5-fold faster, this same cycle
would be reduced to around ~2 sec. A 2 sec cycle rate will allow
for at least three data points to be acquired over even a very sharp
6 sec chromatographic peak. The combined precursor and neutral
loss experiment is also particularly effective because few metabolic
changes would affect both the neutral and charged parts of the
molecule, allowing for broad metabolite detection. In the case of
many co-eluting metabolites, a greater number of dependent EPI
scans is possible based on the 5-fold faster LIT scan rate, which
will also allow for more MS/MS spectra to be acquired in a single
injection. Thus the dual scan survey with four EPI scans can allow
for greater metabolite detection without compromising scan rate.
(Dynamic Background Subtraction (DBS) is another way to ensure
efficient ion selection during an automated experiment and
doesn’t require more than one dependent EPI scan, significantly
reducing the cycle time.) This optimal mode is only possible on a
QTRAP 5500 system. Similarly to the dual survey mode, pMRM
experiments also benefit from a 4-fold reduction in scan rate
compared to the 4000 QTRAP system in much the same way.
EARLY DISCOVERY
EARLY DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Figure 2: Sensitivity gains on the QTRAP® 5500 LC/MS/MS System over the 4000 QTRAP® System. Three metabolites with corresponding linear ion trap (LIT) MS/MS spectra are
compared for the QTRAP 5500 system (left panels) and the 4000 QTRAP system (right panels.) In each case, a significant increase in the number of interpretable fragments is
obtained by the QTRAP 5500 system.
32
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
33
Another differential feature is the LIT MS/MS sensitivity. Compared
to the 4000 QTRAP system, an improvement of 10- to 100-fold
in intensity is apparent for data collected on the QTRAP 5500
system. This has two main benefits, with the first being the
detection of metabolites at an unprecedented low level. Often in
in vivo pharmacokinetic work, MRM methods track metabolites
at very low levels. For the first time, the QTRAP 5500 system
can now allow qualitative identification of metabolites at nearly
equivalent levels. The second benefit is the acquisition of greater
number of fragments available for detailed structural identification
on the QTRAP 5500 system; the presence of more fragments
aids in greater structural refinement of interpretation. Figure 2
shows an example of the QTRAP 5500 system and 4000 QTRAP
system in terms of the number of interpretable fragments for the
same metabolites.
To illustrate these points, a nefazodone incubation was analyzed
on the QTRAP 5500 system, and two survey modes were used:
1) pMRM information-dependent acquisition (IDA) and 2) dual
scan precursor and neutral loss IDA. A number of highly refined
MS/MS confirmations will be presented to demonstrate the level
of refinement possible using the high sensitivity LIT MS/MS of the
new QTRAP 5500 system.
Experimental conditions
Samples for analysis:
Control and incubations (20 µL each) were diluted with 200
µL 95% (5.0 mM ammonium formate)/5% ACN (v/v) prior to
injection. After dilution, the final concentration of nefazodone in
the control solution was 2.5 µM.
Chromatography (LC)
System: Shimadzu Prominence (Shimadzu Corp,
Kyoto, Japan)
Column:
Stable Bond C18 (4.6 mm ID x 50 mm) 1.8 µm (Agilent, Palo Alto, CA)
Mobile phase: A) 0.1% Formic Acid
B) Acetonitrile + 0.1% Formic Acid
Column oven
temperature: 45 ºC
Flow rate:
1.00 mL/min
Injection
volume:
10 µL
Gradient
runtime: 10.0 min (Table 1)
Mass spectrometry
Incubations
Solutions:
50 mM phosphate buffer (pH 7.4)
5.0 mM NADPH in phosphate buffer
Solution A. 2.0 mg/mL rat microsomes in phosphate buffer
Solution B. 20 µM nefazodone in 5.0 mM NADPH
Solution C. Acetronitrile
Control (t = 0 min):
200 µL A, 200 µL B, and 400 µL C were added to a 1.8 mL
Eppendorf tube (final volume, 800 µL)
EARLY DISCOVERY
EARLY DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Step
Time
(min)
Flow Rate
(µL/min)
%A
%B
0
0.0
1,000
95
5
1
1.0
1,000
95
5
2
2.0
1,000
70
30
3
10.0
1,000
60
40
4
10.1
1,000
5
95
95
5
10.5
1,000
5
6
10.6
1,000
95
5
7
15.0
1,000
95
5
Figure 3: Sample/control comparison in LightSight® Software. In this example, a
low level of N-dealkylation, oxidation, and bis-dehydrogenation metabolite was
observed. The MS/MS comparison list (middle right) allows for a quick comparison of
metabolite versus parent fragmentation patterns. The LIT MS/MS sensitivity QTRAP®
5500 System is excellent for the detection of low-level (0.1%) metabolites based on
the MRM total ion current (TIC).
Figure 4: Fragment analysis of the same N-dealkylation, oxidation, and bisdehydrogenation metabolites from Figure 3. The ACD fragmenter overlays structural
fragment assignments on top of the MS spectra. The user can then scroll through the
list deselecting any incorrect predictions.
Results
Figure 4 shows the fragment assignment accomplished using
the ACD Fragmenter. In this case, 80% of the fragments were
assigned using the automated workflow. The metabolically
introduced carbonyl group is likely to be on the 6, 7, 8, or 12
carbon atom.
A number of novel metabolites will be identified that
demonstrate the ultra-high sensitivity of the LIT mode on the
QTRAP 5500 system. Initial screening of the metabolites was
accomplished by sample and control comparison using LightSight
Software. Figure 3 shows the extracted ion chromatogram (XIC)
trace, MS/MS spectrum, and other relevant information in the
LightSight Software interface for an N-dealkylation, oxidation,
and bis-dehydrogenation metabolite, representing 0.1% of the
total ion chromatogram (TIC) area, a good example of a lowlevel species. Even for this trace-level metabolite, 18 high-quality
fragments are apparent in the spectrum. LightSight Software
2.1 allows spectra to be directly imported into ACD Spectrum
Manager and Fragmenter™ for MS/MS automated analysis.
One of the other interesting metabolic changes is, an
N-dealkylation plus oxidation and dehydrogenation, found at
a retention time of 4.13 minutes. Figure 5 shows the XIC and
MS/MS spectra for fragments identified by LightSight Software.
Examination of the MS/MS spectra reveals 19 fragments of
high quality. The ACD Fragmenter was able to assign 70% of
these fragments to the predicted structure with minimal manual
review (Figure 6). The metabolically introduced ketone group is
likely to be at positions 21, 20, 11, and 10 on the backbone of
the structure.
Table 1: Gradient composition.
The tube was vortexed for 1 min, incubated 2-3 min on ice, and
then centrifuged for 2 min at 10,000 rpm.
Final concentration of nefazodone is 5.0 µM in 1.25 mM NADPH
and 25 mM phosphate buffer.
Incubation (t = 60 min):
200 µL A and 200 µL B were added to a 1.8 mL
Eppendorf tube (final volume, 800 µL)
This mixture was incubated for 60 min at 37 °C with shaking
(Eppendorf apparatus, 350 rpm agitation)
The reaction was quenched with 400 µL of C
The QTRAP 5500 system and LightSight Software 2.1 were used
for all LC/MS experiments. A single injection pMRM approach
was employed, triggering the collection of confirmatory MS/MS
spectra in an automated experiment (IDA). A second injection
used a dual scan parent precursor/neutral loss IDA method,
although no major metabolites were missed in the first run. In
both cases, the 20,000 Da/sec scan rate was used with four
dependent EPI scans. All methods were generated using the
LightSight Software 2.1 automatic method builder.
The mixture was vortexed for 1 min, incubated on ice for 2-3 min,
and then centrifuged for 2 min at 10,000 rpm.
Figure 5: Extracted ion chromatogram (XIC) and MS/MS spectra for a N-dealkylation fragment of nefazodone. A peak representing the N-dealkylation of the chlorinecontaining ring on the left half of nefazodone is shown and includes additional sites of oxidation and dehydrogenation. The high LIT sensitivity of the QTRAP® 5500 System
provides superior fragmentation detail.
34
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
35
Another differential feature is the LIT MS/MS sensitivity. Compared
to the 4000 QTRAP system, an improvement of 10- to 100-fold
in intensity is apparent for data collected on the QTRAP 5500
system. This has two main benefits, with the first being the
detection of metabolites at an unprecedented low level. Often in
in vivo pharmacokinetic work, MRM methods track metabolites
at very low levels. For the first time, the QTRAP 5500 system
can now allow qualitative identification of metabolites at nearly
equivalent levels. The second benefit is the acquisition of greater
number of fragments available for detailed structural identification
on the QTRAP 5500 system; the presence of more fragments
aids in greater structural refinement of interpretation. Figure 2
shows an example of the QTRAP 5500 system and 4000 QTRAP
system in terms of the number of interpretable fragments for the
same metabolites.
To illustrate these points, a nefazodone incubation was analyzed
on the QTRAP 5500 system, and two survey modes were used:
1) pMRM information-dependent acquisition (IDA) and 2) dual
scan precursor and neutral loss IDA. A number of highly refined
MS/MS confirmations will be presented to demonstrate the level
of refinement possible using the high sensitivity LIT MS/MS of the
new QTRAP 5500 system.
Experimental conditions
Samples for analysis:
Control and incubations (20 µL each) were diluted with 200
µL 95% (5.0 mM ammonium formate)/5% ACN (v/v) prior to
injection. After dilution, the final concentration of nefazodone in
the control solution was 2.5 µM.
Chromatography (LC)
System: Shimadzu Prominence (Shimadzu Corp,
Kyoto, Japan)
Column:
Stable Bond C18 (4.6 mm ID x 50 mm) 1.8 µm (Agilent, Palo Alto, CA)
Mobile phase: A) 0.1% Formic Acid
B) Acetonitrile + 0.1% Formic Acid
Column oven
temperature: 45 ºC
Flow rate:
1.00 mL/min
Injection
volume:
10 µL
Gradient
runtime: 10.0 min (Table 1)
Mass spectrometry
Incubations
Solutions:
50 mM phosphate buffer (pH 7.4)
5.0 mM NADPH in phosphate buffer
Solution A. 2.0 mg/mL rat microsomes in phosphate buffer
Solution B. 20 µM nefazodone in 5.0 mM NADPH
Solution C. Acetronitrile
Control (t = 0 min):
200 µL A, 200 µL B, and 400 µL C were added to a 1.8 mL
Eppendorf tube (final volume, 800 µL)
EARLY DISCOVERY
EARLY DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Step
Time
(min)
Flow Rate
(µL/min)
%A
%B
0
0.0
1,000
95
5
1
1.0
1,000
95
5
2
2.0
1,000
70
30
3
10.0
1,000
60
40
4
10.1
1,000
5
95
95
5
10.5
1,000
5
6
10.6
1,000
95
5
7
15.0
1,000
95
5
Figure 3: Sample/control comparison in LightSight® Software. In this example, a
low level of N-dealkylation, oxidation, and bis-dehydrogenation metabolite was
observed. The MS/MS comparison list (middle right) allows for a quick comparison of
metabolite versus parent fragmentation patterns. The LIT MS/MS sensitivity QTRAP®
5500 System is excellent for the detection of low-level (0.1%) metabolites based on
the MRM total ion current (TIC).
Figure 4: Fragment analysis of the same N-dealkylation, oxidation, and bisdehydrogenation metabolites from Figure 3. The ACD fragmenter overlays structural
fragment assignments on top of the MS spectra. The user can then scroll through the
list deselecting any incorrect predictions.
Results
Figure 4 shows the fragment assignment accomplished using
the ACD Fragmenter. In this case, 80% of the fragments were
assigned using the automated workflow. The metabolically
introduced carbonyl group is likely to be on the 6, 7, 8, or 12
carbon atom.
A number of novel metabolites will be identified that
demonstrate the ultra-high sensitivity of the LIT mode on the
QTRAP 5500 system. Initial screening of the metabolites was
accomplished by sample and control comparison using LightSight
Software. Figure 3 shows the extracted ion chromatogram (XIC)
trace, MS/MS spectrum, and other relevant information in the
LightSight Software interface for an N-dealkylation, oxidation,
and bis-dehydrogenation metabolite, representing 0.1% of the
total ion chromatogram (TIC) area, a good example of a lowlevel species. Even for this trace-level metabolite, 18 high-quality
fragments are apparent in the spectrum. LightSight Software
2.1 allows spectra to be directly imported into ACD Spectrum
Manager and Fragmenter™ for MS/MS automated analysis.
One of the other interesting metabolic changes is, an
N-dealkylation plus oxidation and dehydrogenation, found at
a retention time of 4.13 minutes. Figure 5 shows the XIC and
MS/MS spectra for fragments identified by LightSight Software.
Examination of the MS/MS spectra reveals 19 fragments of
high quality. The ACD Fragmenter was able to assign 70% of
these fragments to the predicted structure with minimal manual
review (Figure 6). The metabolically introduced ketone group is
likely to be at positions 21, 20, 11, and 10 on the backbone of
the structure.
Table 1: Gradient composition.
The tube was vortexed for 1 min, incubated 2-3 min on ice, and
then centrifuged for 2 min at 10,000 rpm.
Final concentration of nefazodone is 5.0 µM in 1.25 mM NADPH
and 25 mM phosphate buffer.
Incubation (t = 60 min):
200 µL A and 200 µL B were added to a 1.8 mL
Eppendorf tube (final volume, 800 µL)
This mixture was incubated for 60 min at 37 °C with shaking
(Eppendorf apparatus, 350 rpm agitation)
The reaction was quenched with 400 µL of C
The QTRAP 5500 system and LightSight Software 2.1 were used
for all LC/MS experiments. A single injection pMRM approach
was employed, triggering the collection of confirmatory MS/MS
spectra in an automated experiment (IDA). A second injection
used a dual scan parent precursor/neutral loss IDA method,
although no major metabolites were missed in the first run. In
both cases, the 20,000 Da/sec scan rate was used with four
dependent EPI scans. All methods were generated using the
LightSight Software 2.1 automatic method builder.
The mixture was vortexed for 1 min, incubated on ice for 2-3 min,
and then centrifuged for 2 min at 10,000 rpm.
Figure 5: Extracted ion chromatogram (XIC) and MS/MS spectra for a N-dealkylation fragment of nefazodone. A peak representing the N-dealkylation of the chlorinecontaining ring on the left half of nefazodone is shown and includes additional sites of oxidation and dehydrogenation. The high LIT sensitivity of the QTRAP® 5500 System
provides superior fragmentation detail.
34
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
35
EARLY DISCOVERY
EARLY DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Figure 6: The fragmentation pattern of an N-demethylation metabolite of nefazodone. Fragmentation of a nefazodine metabolite with an N-dealkylation of the chlorinecontaining ring on the left half of the molecule with additional sites of oxidation and dehydrogenation. ACD Fragmenter™ confirmed around 70 percent of the major
fragments automatically. The data suggests that the ketone group may be found at positions 21, 20, 10, and 11 on nefazodone’s aliphatic backbone.
Figure 8: Structural assignment of the m/z 274 fragment from nefazodone. The ACD Fragmenter™ assigns the m/z 274 nefazodone fragment to the right half the molecule.
This is consistent with oxidative dechlorination taking place on the left ring. Positions 29, 30, or 31 are the most likely sites for oxidation.
Figure 7: XIC and MS/MS spectra of a nefazodone metabolite displaying of oxidative de-chlorination identified using LightSight® Software.
Figure 9: Structural assignment of the m/z 246 fragment of nefazodone. The ACD Fragmenter™ assigns the m/z 246 nefazodone fragment to the right half the molecule. This
is consistent with oxidative dechlorination taking place on the left ring. Position 29, 30, or 31 are the most likely sites for oxidation.
Two novel metabolites that were displaying oxidative and
dechlorination changes were detected by pMRM IDA. Figure 7
shows an example of the oxidative dechlorination metabolite
found at 4.25 min in the LC run. The LIT MS/MS spectrum
shows 14 diagnostic fragments at excellent sensitivity.
Figures 8 and 9 contain the ACD Fragmenter assignments
for this metabolite.
36
RUO-MKT-01-1583-A
Conclusions
•
•
•
The QTRAP 5500 system has excellent sensitivity for both detection and
confirmation of metabolites
The pMRM method as well as dual scan precursor and neutral loss IDA
methods, provide comprehensive detection of in vitro metabolites in a
single injection. In most cases, a single injection using pMRM methods
alone is more than adequate for a complete analysis.
DRUG METABOLISM
www.absciex.com
•
The 10- to 100-fold LIT MS/MS sensitivity improvement allows for
superior detection of fragments that was not possible on less sensitive
trap or TOF instruments. This greater degree of fragmentation allows
for a high level of structural assignment.
The scan rate is improved 4- or 5-fold over previous QTRAP systems,
allowing for greater IDA coverage, even with sharp peaks delivered
by the most progressive small particle high-pressure chromatography
methods.
www.absciex.com
References
1
Peterman, S. M.; Duczak, N. J.; Kalgutkar, A. S.; Lame, M. E. ; Soglia, J. R. J Am Soc Mass
Spectrom 2006; 17: 363.
2
Li, A. C.; Gohdes, M. A.; Shou, W. Z. Rapid Commun Mass Spectrom 2007; 21: 1421.
3
Li, A. C.; Shou, W. Z.; Mai, T. T.; Jiang, X.y. Rapid Commun Mass Spectrom 2007; 21: 4001.
DRUG METABOLISM
37
EARLY DISCOVERY
EARLY DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Figure 6: The fragmentation pattern of an N-demethylation metabolite of nefazodone. Fragmentation of a nefazodine metabolite with an N-dealkylation of the chlorinecontaining ring on the left half of the molecule with additional sites of oxidation and dehydrogenation. ACD Fragmenter™ confirmed around 70 percent of the major
fragments automatically. The data suggests that the ketone group may be found at positions 21, 20, 10, and 11 on nefazodone’s aliphatic backbone.
Figure 8: Structural assignment of the m/z 274 fragment from nefazodone. The ACD Fragmenter™ assigns the m/z 274 nefazodone fragment to the right half the molecule.
This is consistent with oxidative dechlorination taking place on the left ring. Positions 29, 30, or 31 are the most likely sites for oxidation.
Figure 7: XIC and MS/MS spectra of a nefazodone metabolite displaying of oxidative de-chlorination identified using LightSight® Software.
Figure 9: Structural assignment of the m/z 246 fragment of nefazodone. The ACD Fragmenter™ assigns the m/z 246 nefazodone fragment to the right half the molecule. This
is consistent with oxidative dechlorination taking place on the left ring. Position 29, 30, or 31 are the most likely sites for oxidation.
Two novel metabolites that were displaying oxidative and
dechlorination changes were detected by pMRM IDA. Figure 7
shows an example of the oxidative dechlorination metabolite
found at 4.25 min in the LC run. The LIT MS/MS spectrum
shows 14 diagnostic fragments at excellent sensitivity.
Figures 8 and 9 contain the ACD Fragmenter assignments
for this metabolite.
36
RUO-MKT-01-1583-A
Conclusions
•
•
•
The QTRAP 5500 system has excellent sensitivity for both detection and
confirmation of metabolites
The pMRM method as well as dual scan precursor and neutral loss IDA
methods, provide comprehensive detection of in vitro metabolites in a
single injection. In most cases, a single injection using pMRM methods
alone is more than adequate for a complete analysis.
DRUG METABOLISM
www.absciex.com
•
The 10- to 100-fold LIT MS/MS sensitivity improvement allows for
superior detection of fragments that was not possible on less sensitive
trap or TOF instruments. This greater degree of fragmentation allows
for a high level of structural assignment.
The scan rate is improved 4- or 5-fold over previous QTRAP systems,
allowing for greater IDA coverage, even with sharp peaks delivered
by the most progressive small particle high-pressure chromatography
methods.
www.absciex.com
References
1
Peterman, S. M.; Duczak, N. J.; Kalgutkar, A. S.; Lame, M. E. ; Soglia, J. R. J Am Soc Mass
Spectrom 2006; 17: 363.
2
Li, A. C.; Gohdes, M. A.; Shou, W. Z. Rapid Commun Mass Spectrom 2007; 21: 1421.
3
Li, A. C.; Shou, W. Z.; Mai, T. T.; Jiang, X.y. Rapid Commun Mass Spectrom 2007; 21: 4001.
DRUG METABOLISM
37
Breakthrough Productivity for ADME Studies
Using the AB SCIEX TripleTOF® 5600 System
Speed, sensitivity, resolution, mass accuracy and linearity – together in one instrument for the first time
Hesham Ghobarah, James Ferguson, and Suma Ramagiri
AB SCIEX, Toronto, Ontario and Framingham, Massachusetts
Key scientific challenges of ADME studies
•
•
•
Productivity – Increasing productivity in drug discovery and
development continues to be a primary goal in pharmaceutical research.
Separate quantitative and qualitative analysis – In the past, the
optimal instrumentation for quantitative analysis was not suitable
for qualitative analysis and vice versa. Typically, one ADME research
group completed metabolite ID studies and another group with doing
clearance studies.
Analysis at non-relevant doses – For many compounds, ADME studies
have been conducted at a higher than therapeutic concentration,
occasionally leading to false conclusions due to concentrationdependent metabolic pathways for some compounds.
•
•
Quant and qual in one run – Combining the best attributes of triple
quadrupole systems (the workhorses of quantitative analysis) with a
high-performance accurate mass analyzer into one instrument makes it
possible to conduct both quantitative and qualitative analyses in
one run.
Triple-quad-like sensitivity – TripleTOF systems show quantitative
sensitivity on par with the API 4000™ LC/MS/MS System, known
industry-wide for its sensitivity and robustness.
Speed – Most ADME labs are selecting UHPLC as the separation
method of choice. UHPLC’s narrower chromatographic peaks result in
higher concentrations at peak apices and, therefore, lower LOQs. To
accommodate the UHPLC time scale, mass spectrometers must be able
to perform the needed experiments on short cycle times. (Typical cycle
times for the TripleTOF system are below 0.5 s).
Key features of TripleTOF systems for ADME analyses
•
•
•
38
Resolution and mass accuracy regardless of scan speed – Instruments
employing orbital trapping require slower scanning to get higher
resolution. The TripleTOF systems maintain approximately 30,000
resolution regardless of analysis speed.
Calibration stability and linearity – Traditional time-of-flight (TOF)
instruments typically require internal calibration to maintain mass
accuracy resulting in lowered signals and non-linear quantitative
response. The TripleTOF systems have been designed to overcome both
of these limitations.
Resolution and mass accuracy even at low m/z – Traditional TOF
instruments provide good mass accuracy and resolution at high m/z.
The high detector speed of the TripleTOF systems allows for sufficient
resolution at low m/z, providing unambiguous formulae assigments
using accurate mass, as well as the isotope and fragmention pattern.
RUO-MKT-01-1583-A
Due to their excellent sensitivity and high throughput, triple
quadrupole instruments have been the workhorse instrument
for the quantitative aspects of ADME analysis. Accurate mass
instruments such as TOF and orbital trapping analyzers have
been used for metabolite identification due to their high mass
accuracy and resolution. Unfortunately, high-resolution orbital
trapping approaches compromises speed at the expense of
resolution. Traditional TOF technology has shown limitations
in linearity and requires internal calibration to maintain
mass accuracy. Modifying study designs, sample preparation
procedures, or chromatography in order to accommodate
for these instrument limitations is not acceptable in a highthroughput laboratory.
Advanced technology enables true quant/qual
without compromises
Key benefits of the TripleTOF® 5600 System for ADME assays
•
compounds need to be analyzed weekly. Therefore, a generic
data acquisition method that eliminates the need to optimize for
individual compounds is highly desirable.
•
•
Integrated software packages – To date, no manufacturer has provided
one software package capable of finding metabolites, confirming the
site of metabolism, tracking time course, experiments and doing interspecies comparisons. MetabolitePilot™ Software does all these tasks.
Smarter acquisition – The automatic acquisition of MS/MS on low- level
metabolites has been elusive before the introduction of real-time mass
defect triggering enabled on the TripleTOF system. Now, the TripleTOF
system can trigger on ions with mass defects related to the parent
drug even at intensities lower than other compounds, allowing the
proverbial needle to be pulled from its haystack.
is highlighted for the AB SCIEX TripleTOF 5600 system.
Modifications of existing assays are not necessary to realize the
benefits of combined quant/qual features in a single experiment, a
true breakthrough in productivity.
Materials and methods
Sample preparation
Sample preparation. Six compounds (clomipramine, diclofenac,
imipramine, haloperidol, verapamil, and midazolam) were
incubated in rat liver microsomes at an initial substrate
concentration of 1 µM in 96-well format. Standard highthroughput incubation conditions were used with time points
of 0, 5, 15, 30, 60, and 120 min. Protein concentration was
1 mg/mL and NADPH was present at 4 mM. Reaction was
quenched using an equal volume of acetonitrile and then diluted
1:1 with water prior to analysis. The final substrate concentration
at t = 0 was 0.25 µM.
EARLY DISCOVERY
EARLY DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Chromatography
The AB SCIEX TripleTOF 5600 system combines the best attributes
of triple quadrupoles and accurate mass analyzers in a single
instrument. The highly innovative design of the AB SCIEX
TripleTOF 5600 system combines a proven high-performance
triple quadrupole front-end with the Accelerator TOF™ analyzer,
a state-of-the-art accurate mass analyzer with unprecedented
performance and stability (Figure 1). This results in the linearity
and sensitivity of a high performance triple quadrupole, combined
with the speed, high mass accuracy (2 ppm or less), and high
resolution (30,000) of an accurate mass instrument, even for lowmass for small molecules.
System: Prominence UFLC-XR HPLC system
Column:
Phenomenex Synergi Polar-RP column
2 × 50 mm, 2.5 µm
Injection
volume:
10 μL
Flow rate:
0.750 ml/min
Mobile phase: A) Water/0.1% formic acid
B) Acetonitrile/0.1% formic acid
A generic gradient was used.
In this technical note, a true quant/qual ADME application
Introduction
Metabolic stability experiments in microsomes are a classic
example of in vitro studies that have traditionally been used
for estimation of hepatic clearance. For compounds exhibiting
rapid metabolism, additional qualitative studies are performed
to identify the major metabolites in order to identify metabolic
“soft spots.” This information aids the medicinal chemist in
optimizing the structure of the unknown metabolite. However,
performing a separate analysis for metabolite identification
is time consuming. A major improvement in productivity can
be achieved if both quantitative clearance and qualitative soft
spot analysis can be performed in a single analytical run. A key
requirement is to obtain accurate mass MS/MS spectra at high
speed on a fast chromatographic time scale. In addition, due
to the high- throughput nature of in vitro assays, hundreds of
DRUG METABOLISM
www.absciex.com
Figure 1: AB SCIEX TripleTOF® 5600 System ion path. Proven front end technology coupled to a state of the art Accelerator TOF™ Analyzer results in unparalleled performance
for both quantitative and qualitative analysis.
www.absciex.com
DRUG METABOLISM
39
Breakthrough Productivity for ADME Studies
Using the AB SCIEX TripleTOF® 5600 System
Speed, sensitivity, resolution, mass accuracy and linearity – together in one instrument for the first time
Hesham Ghobarah, James Ferguson, and Suma Ramagiri
AB SCIEX, Toronto, Ontario and Framingham, Massachusetts
Key scientific challenges of ADME studies
•
•
•
Productivity – Increasing productivity in drug discovery and
development continues to be a primary goal in pharmaceutical research.
Separate quantitative and qualitative analysis – In the past, the
optimal instrumentation for quantitative analysis was not suitable
for qualitative analysis and vice versa. Typically, one ADME research
group completed metabolite ID studies and another group with doing
clearance studies.
Analysis at non-relevant doses – For many compounds, ADME studies
have been conducted at a higher than therapeutic concentration,
occasionally leading to false conclusions due to concentrationdependent metabolic pathways for some compounds.
•
•
Quant and qual in one run – Combining the best attributes of triple
quadrupole systems (the workhorses of quantitative analysis) with a
high-performance accurate mass analyzer into one instrument makes it
possible to conduct both quantitative and qualitative analyses in
one run.
Triple-quad-like sensitivity – TripleTOF systems show quantitative
sensitivity on par with the API 4000™ LC/MS/MS System, known
industry-wide for its sensitivity and robustness.
Speed – Most ADME labs are selecting UHPLC as the separation
method of choice. UHPLC’s narrower chromatographic peaks result in
higher concentrations at peak apices and, therefore, lower LOQs. To
accommodate the UHPLC time scale, mass spectrometers must be able
to perform the needed experiments on short cycle times. (Typical cycle
times for the TripleTOF system are below 0.5 s).
Key features of TripleTOF systems for ADME analyses
•
•
•
38
Resolution and mass accuracy regardless of scan speed – Instruments
employing orbital trapping require slower scanning to get higher
resolution. The TripleTOF systems maintain approximately 30,000
resolution regardless of analysis speed.
Calibration stability and linearity – Traditional time-of-flight (TOF)
instruments typically require internal calibration to maintain mass
accuracy resulting in lowered signals and non-linear quantitative
response. The TripleTOF systems have been designed to overcome both
of these limitations.
Resolution and mass accuracy even at low m/z – Traditional TOF
instruments provide good mass accuracy and resolution at high m/z.
The high detector speed of the TripleTOF systems allows for sufficient
resolution at low m/z, providing unambiguous formulae assigments
using accurate mass, as well as the isotope and fragmention pattern.
RUO-MKT-01-1583-A
Due to their excellent sensitivity and high throughput, triple
quadrupole instruments have been the workhorse instrument
for the quantitative aspects of ADME analysis. Accurate mass
instruments such as TOF and orbital trapping analyzers have
been used for metabolite identification due to their high mass
accuracy and resolution. Unfortunately, high-resolution orbital
trapping approaches compromises speed at the expense of
resolution. Traditional TOF technology has shown limitations
in linearity and requires internal calibration to maintain
mass accuracy. Modifying study designs, sample preparation
procedures, or chromatography in order to accommodate
for these instrument limitations is not acceptable in a highthroughput laboratory.
Advanced technology enables true quant/qual
without compromises
Key benefits of the TripleTOF® 5600 System for ADME assays
•
compounds need to be analyzed weekly. Therefore, a generic
data acquisition method that eliminates the need to optimize for
individual compounds is highly desirable.
•
•
Integrated software packages – To date, no manufacturer has provided
one software package capable of finding metabolites, confirming the
site of metabolism, tracking time course, experiments and doing interspecies comparisons. MetabolitePilot™ Software does all these tasks.
Smarter acquisition – The automatic acquisition of MS/MS on low- level
metabolites has been elusive before the introduction of real-time mass
defect triggering enabled on the TripleTOF system. Now, the TripleTOF
system can trigger on ions with mass defects related to the parent
drug even at intensities lower than other compounds, allowing the
proverbial needle to be pulled from its haystack.
is highlighted for the AB SCIEX TripleTOF 5600 system.
Modifications of existing assays are not necessary to realize the
benefits of combined quant/qual features in a single experiment, a
true breakthrough in productivity.
Materials and methods
Sample preparation
Sample preparation. Six compounds (clomipramine, diclofenac,
imipramine, haloperidol, verapamil, and midazolam) were
incubated in rat liver microsomes at an initial substrate
concentration of 1 µM in 96-well format. Standard highthroughput incubation conditions were used with time points
of 0, 5, 15, 30, 60, and 120 min. Protein concentration was
1 mg/mL and NADPH was present at 4 mM. Reaction was
quenched using an equal volume of acetonitrile and then diluted
1:1 with water prior to analysis. The final substrate concentration
at t = 0 was 0.25 µM.
EARLY DISCOVERY
EARLY DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Chromatography
The AB SCIEX TripleTOF 5600 system combines the best attributes
of triple quadrupoles and accurate mass analyzers in a single
instrument. The highly innovative design of the AB SCIEX
TripleTOF 5600 system combines a proven high-performance
triple quadrupole front-end with the Accelerator TOF™ analyzer,
a state-of-the-art accurate mass analyzer with unprecedented
performance and stability (Figure 1). This results in the linearity
and sensitivity of a high performance triple quadrupole, combined
with the speed, high mass accuracy (2 ppm or less), and high
resolution (30,000) of an accurate mass instrument, even for lowmass for small molecules.
System: Prominence UFLC-XR HPLC system
Column:
Phenomenex Synergi Polar-RP column
2 × 50 mm, 2.5 µm
Injection
volume:
10 μL
Flow rate:
0.750 ml/min
Mobile phase: A) Water/0.1% formic acid
B) Acetonitrile/0.1% formic acid
A generic gradient was used.
In this technical note, a true quant/qual ADME application
Introduction
Metabolic stability experiments in microsomes are a classic
example of in vitro studies that have traditionally been used
for estimation of hepatic clearance. For compounds exhibiting
rapid metabolism, additional qualitative studies are performed
to identify the major metabolites in order to identify metabolic
“soft spots.” This information aids the medicinal chemist in
optimizing the structure of the unknown metabolite. However,
performing a separate analysis for metabolite identification
is time consuming. A major improvement in productivity can
be achieved if both quantitative clearance and qualitative soft
spot analysis can be performed in a single analytical run. A key
requirement is to obtain accurate mass MS/MS spectra at high
speed on a fast chromatographic time scale. In addition, due
to the high- throughput nature of in vitro assays, hundreds of
DRUG METABOLISM
www.absciex.com
Figure 1: AB SCIEX TripleTOF® 5600 System ion path. Proven front end technology coupled to a state of the art Accelerator TOF™ Analyzer results in unparalleled performance
for both quantitative and qualitative analysis.
www.absciex.com
DRUG METABOLISM
39
Mass spectrometry
A generic method was used for data acquisition on all compounds
and samples. The method consisted of a TOF MS survey scan
followed by two IDA TOF MS/MS scans. The mass range was m/z
100–1000 for both MS and MS/MS. An accumulation time of 100
ms was used for each scan. Dynamic background subtraction was
applied for IDA criteria, and a collision energy of 35 eV with a
spread of +/-10 eV was used for the MS/MS scans. External mass
calibration was performed automatically using a calibrant delivery
system. Data processing was performed using MultiQuant™
Software and MetabolitePilot Software (Figure 2).
Results – sensitivity and speed
MultiQuant Software was used to process the TOF MS
data and generate all quantitative information. An XIC window
of ±10mDa was used for all compounds. Peak areas were
used to plot the % remaining relative to t = 0 (Figure 3),
and MetabolitePilot Software was used to process the
data for metabolites.
Metabolic stability studies are best performed at a lower substrate
concentration (1 µM), which is more physiologically relevant.
Enough sensitivity is required to detect at least 1% of remaining
parent molecule in order to obtain meaningful kinetic data. The
TripleTOF 5600 system demonstrated excellent sensitivity and
speed. For example, 0.1% of midazolam was easily detected
(Figure 4). This represents a concentration of 0.25 nM
or 0.8 pg on column.
Sensitivity does not mean compromising on speed with a
TripleTOF system. A minimum of eight points was obtained in
IDA mode (with two dependent MS/MS scans) across a 4 sec
peak while maintaining 30K resolution in both MS and
MS/MS mode. In fact, each data point consisted of three scans
(one TOF MS scan and two MS/MS scans) for a total of 24
experiments done in less than 4 sec.
High resolution and mass accuracy
No compromises
Excellent mass accuracy and resolution were achieved in both
MS and MS/MS scans (Figure 5). The high mass accuracy obtained
in MS/MS scans greatly facilitates structure elucidation because it
allows unambiguous assignment of elemental composition.
Successful application of quant/qual technology to in vitro
ADME was demonstrated using the TripleTOF 5600 system. No
compromises were made in resolution in order to achieve speed
or sensitivity. A relatively low substrate concentration of 1 µm
was successfully used.
Using MetabolitePilot Software, data from multiple samples
and compounds was processed unattended in batch mode.
The software reported metabolites detected and elemental
compositions in a concise and powerful, yet user-friendly,
interface (Figure 2).
Standard incubation and HPLC conditions were used with no
need to make any modifications in order to gain the benefits
of quant/qual.
EARLY DISCOVERY
EARLY DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Excellent separation, metabolite coverage, and speed were
demonstrated using a generic IDA method (Figure 6).
Figure 5: Mass accuracy and resolution in TOF MS and MS/MS Modes. The MS scan of haloperidol parent from the 5 min time point shows excellent accuracy and
resolution (left panel) with a near perfect match of isotope pattern (inset). The product ion spectrum of hydroxy midazolam from the 5 min time point acquired with IDA is
shown (right panel). Excellent mass accuracy and 30K resolution allow easy assignment of elemental composition, even to the product ions.
Figure 2: Easy data processing in MetabolitePilot™ Software. Using this nextgeneration software simplifies accurate mass metabolite identification with its
automated data processing in batch mode.
Figure 4: Sensitivity and Speed. The 30 minutes time point from Midazolam
microsome incubation with 0.1% of parent remaining (0.8 pg on column). Analyte is
easily detected in TOF MS IDA mode with 8 data points. Each timepoint consists of a
survey scan and two MS/MS dependent scans.
Figure 3: Data Processing in MultiQuant™ Software. (a) TOF MS data was easily processed for multiple analytes to obtain quantitative data. (b) Metabolic stability profiles of 6
common substrates incubated at 1 µM and analyzed using TOF MS with IDA triggered MS/MS.
40
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
Figure 6: Broad coverage for phase I metabolites. Overlay of accurate mass XICs for imipramine incubation at 5 min. A wide range of imipramine phase I metabolites were
easily detected using a completely generic TOF MS IDA method in under 2.5 min.
www.absciex.com
DRUG METABOLISM
41
Mass spectrometry
A generic method was used for data acquisition on all compounds
and samples. The method consisted of a TOF MS survey scan
followed by two IDA TOF MS/MS scans. The mass range was m/z
100–1000 for both MS and MS/MS. An accumulation time of 100
ms was used for each scan. Dynamic background subtraction was
applied for IDA criteria, and a collision energy of 35 eV with a
spread of +/-10 eV was used for the MS/MS scans. External mass
calibration was performed automatically using a calibrant delivery
system. Data processing was performed using MultiQuant™
Software and MetabolitePilot Software (Figure 2).
Results – sensitivity and speed
MultiQuant Software was used to process the TOF MS
data and generate all quantitative information. An XIC window
of ±10mDa was used for all compounds. Peak areas were
used to plot the % remaining relative to t = 0 (Figure 3),
and MetabolitePilot Software was used to process the
data for metabolites.
Metabolic stability studies are best performed at a lower substrate
concentration (1 µM), which is more physiologically relevant.
Enough sensitivity is required to detect at least 1% of remaining
parent molecule in order to obtain meaningful kinetic data. The
TripleTOF 5600 system demonstrated excellent sensitivity and
speed. For example, 0.1% of midazolam was easily detected
(Figure 4). This represents a concentration of 0.25 nM
or 0.8 pg on column.
Sensitivity does not mean compromising on speed with a
TripleTOF system. A minimum of eight points was obtained in
IDA mode (with two dependent MS/MS scans) across a 4 sec
peak while maintaining 30K resolution in both MS and
MS/MS mode. In fact, each data point consisted of three scans
(one TOF MS scan and two MS/MS scans) for a total of 24
experiments done in less than 4 sec.
High resolution and mass accuracy
No compromises
Excellent mass accuracy and resolution were achieved in both
MS and MS/MS scans (Figure 5). The high mass accuracy obtained
in MS/MS scans greatly facilitates structure elucidation because it
allows unambiguous assignment of elemental composition.
Successful application of quant/qual technology to in vitro
ADME was demonstrated using the TripleTOF 5600 system. No
compromises were made in resolution in order to achieve speed
or sensitivity. A relatively low substrate concentration of 1 µm
was successfully used.
Using MetabolitePilot Software, data from multiple samples
and compounds was processed unattended in batch mode.
The software reported metabolites detected and elemental
compositions in a concise and powerful, yet user-friendly,
interface (Figure 2).
Standard incubation and HPLC conditions were used with no
need to make any modifications in order to gain the benefits
of quant/qual.
EARLY DISCOVERY
EARLY DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Excellent separation, metabolite coverage, and speed were
demonstrated using a generic IDA method (Figure 6).
Figure 5: Mass accuracy and resolution in TOF MS and MS/MS Modes. The MS scan of haloperidol parent from the 5 min time point shows excellent accuracy and
resolution (left panel) with a near perfect match of isotope pattern (inset). The product ion spectrum of hydroxy midazolam from the 5 min time point acquired with IDA is
shown (right panel). Excellent mass accuracy and 30K resolution allow easy assignment of elemental composition, even to the product ions.
Figure 2: Easy data processing in MetabolitePilot™ Software. Using this nextgeneration software simplifies accurate mass metabolite identification with its
automated data processing in batch mode.
Figure 4: Sensitivity and Speed. The 30 minutes time point from Midazolam
microsome incubation with 0.1% of parent remaining (0.8 pg on column). Analyte is
easily detected in TOF MS IDA mode with 8 data points. Each timepoint consists of a
survey scan and two MS/MS dependent scans.
Figure 3: Data Processing in MultiQuant™ Software. (a) TOF MS data was easily processed for multiple analytes to obtain quantitative data. (b) Metabolic stability profiles of 6
common substrates incubated at 1 µM and analyzed using TOF MS with IDA triggered MS/MS.
40
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
Figure 6: Broad coverage for phase I metabolites. Overlay of accurate mass XICs for imipramine incubation at 5 min. A wide range of imipramine phase I metabolites were
easily detected using a completely generic TOF MS IDA method in under 2.5 min.
www.absciex.com
DRUG METABOLISM
41
Conclusions
•
•
•
•
EARLY DISCOVERY
EARLY DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
TripleTOF 5600 system is the first accurate mass instrument to
offer quantitative performance comparable to a high performance
triple quadrupole.
The combination of speed, sensitivity, mass accuracy, and resolution
enables routine quant/qual analysis. For metabolic stability, the ability to
perform parent quantification, metabolite detection, and accurate mass
information all in a single run.
MultiQuant and MetabolitePilot Software are powerful tools
for efficient analysis of quantitative and qualitative data.
With proven front-end technology and high performance, the TripleTOF
5600 system fits well with existing high-throughput sample preparation
and fast chromatography.
Acknowledgements
The authors would like to thank Dr. Christie Hunter and Tanya
Gamble for their invaluable contributions and review.
42
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
43
Conclusions
•
•
•
•
EARLY DISCOVERY
EARLY DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
TripleTOF 5600 system is the first accurate mass instrument to
offer quantitative performance comparable to a high performance
triple quadrupole.
The combination of speed, sensitivity, mass accuracy, and resolution
enables routine quant/qual analysis. For metabolic stability, the ability to
perform parent quantification, metabolite detection, and accurate mass
information all in a single run.
MultiQuant and MetabolitePilot Software are powerful tools
for efficient analysis of quantitative and qualitative data.
With proven front-end technology and high performance, the TripleTOF
5600 system fits well with existing high-throughput sample preparation
and fast chromatography.
Acknowledgements
The authors would like to thank Dr. Christie Hunter and Tanya
Gamble for their invaluable contributions and review.
42
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
43
Rapid Metabolite Identification Using
MetabolitePilot™ Software and the TripleTOF®
5600 System
Easily find, identify, and confirm metabolites in accurate mass data
Carmai Seto, Tanya Gamble, and Hesham Ghobarah
AB SCIEX, Concord, Ontario, Canada
Key scientific challenges of metabolite ID workflows
•
•
•
High-performance, integrated software – In the past, the work of
finding, confirming, and quantifying metabolites has required the use of
multiple software packages. MetabolitePilot™ Software can perform all
three functions in one software package.
Complicated data correlation – Typically after processing quantitative
data another software package must be used to correlate data,
(e.g., interspecies comparisons, lot-to-lot variations, time
course studies).
Structural elucidation – The ability to match fragments to proposed
structures speeds the structural elucidation process, providing critical
“soft-spot” information to the medicinal chemist in a timely manner.
based on the structure of the parent compound. It also
automatically calculates mass-defect filters for phase I metabolites,
including the parent drug, as well as phase II and potential
cleavage metabolites (Figure 1).
MetabolitePilot Software makes the work of identifying and
confirming metabolites more efficient with an easy-to-use,
integrated workspace that displays all relevant information.
The software displays the analog, MS, and MS/MS data
associated with each metabolite in a single view (Figure 2).
A confirmation score is also provided to facilitiate metabolite
peak detection (Figure 3). To aid with the confirmation of
metabolite peak identification, the software enables faster
comparison of metabolite and parent product ion spectra with
a simple and effective display. The product ion spectrum of the
parent drug is automatically compared with each metabolite,
and the results of the comparison are displayed in the MS/MS
panel of the results workspace.
Materials and methods
Sample preparation
Bromocriptine was incubated in rat liver microsomes under
oxidative conditions at 37° C for 1 hr, along with a control. The
reactions were quenched by the addition of an equal volume of
acetonitrile to the microsomal solutions and then centrifuged at
14,000 rpm for 5 min.
Chromatography
The incubations were analyzed using a Shimadzu Prominence
UFLC-XR LC system with UV detector set at 302 nm. All analyses
were performed using an Imtakt Cadenza CD-C18 (2×50 mm)
3µm, ODS column that was held at 40° C. Solvent A contained
water with 0.1% formic acid and solvent B contained acetonitrile
with 0.1% formic acid, and the flow rate was set at 600 µL/min.
The gradient is summarized in Table 1.
Gradient:Time Gradient
(min)
composition (%B)
Key benefits of MetabolitePilot Software
•
•
•
•
Speed – Data can be batch-processed, saving time during analysis.
A comprehensive software package – MetabolitePilot Software
finds metabolites, speeds confirmation the site of metabolism with
fragmentation interpretation tools, and correlates data across userdefined domains, with multiple options for visualizing the data.
Structure-driven processing – Processing parameters are based on the
structure of the parent drug and can be rapidly and easily built.
Correlation with analog (UV, DAD, or radioactivity) data – Peak areas
from both mass spectrometry and analog data are both provided.
Key features of MetabolitePilot Software
•
•
•
•
Multiple methods for finding metabolites – Data can be mined for
expected and unexpected metabolites (unpredicted chromatographic
peaks), as well as by mass defect, isotope patterns and similarity of the
product ion spectra with that of the parent drug.
Data comparisons against up to five controls – For example, during GSH
screenings, the software encorporates a t0 control, a second control
without the test article, a third control without GSH, and another
control without NADPH.
Integrated MS/MS fragment interpretation and structure editing –
These features speed the determination of the site of metabolism.
Weighted scoring – Scoring may be customized and is based on mass
accuracy, isotopic pattern fidelity, similarity and quality of the MS/MS
data to that of the parent drug, and mass-defect matching.
Introduction
The ability to find, identify, and confirm metabolites as quickly
as possible is critical at multiple stages of drug discovery and
development. Advances in instrumentation have enabled the
generation of very information-rich raw data. New software
44
RUO-MKT-01-1583-A
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
programs that automate portions of the metabolite identification
workflow provide for considerable benefits to drug metabolism
researchers. For example, accurate mass instruments such as
the time-of-flight (TOF) MS have the capability to provide both
qualitative and quantitative information in a single analysis.
However, the data analysis can be quite complicated and very
time consuming if performed manually, especially if analog data
was acquired along with MS and MS/MS data. Software programs
can automate the process of finding, identifying, and confirming
metabolites in accurate mass data. This technical note describes
how MetabolitePilot Software and the AB SCIEX TripleTOF® 5600
System can make the process of identifying and confirming
metabolites more efficient than ever before.
Figure 1: Multiple mass-defect filtering setup in the processing parameters.
MetabolitePilot™ Software automatically calculates the mass-defect filters for multiple
metabolite classes using the parent structure.
DRUG METABOLISM
www.absciex.com
10
0.5
10
0.75
20
5.5
40
6.5
60
6.8
90
6.9
90
7
10
Table 1: LC gradient conditions.
Mass spectrometry
The TripleTOF 5600 system method used for data acquisition
consisted of a TOF MS survey scan followed by two IDA TOF MS/
MS scans. Mass Defect triggered IDA with Dynamic Background
Subtraction™ algorithm was used to collect the MS/MS data.
External mass calibration was performed automatically using a
calibrant delivery system.
MetabolitePilot Software: Automated identification and
confirmation of metabolites
MetabolitePilot Software employs multiple, powerful data
processing algorithms to perform both non-targeted and targeted
processing in parallel. In addition, generic peak-finding, multiple
mass-defect filtering, isotope-pattern matching, and metabolite
detection based on common product ions or neutral losses
can all be used simultaneously to find and identify metabolites
efficiently. To harness the power of these algorithms, the software
automatically calculates the most appropriate compound-specific
processing parameters for a given compound. MetabolitePilot
Software automatically predicts potential cleavage metabolites
0
Figure 2: Results workspace of MetabolitePilot™ Software. All data is displayed in a
single workspace for efficient data review.
www.absciex.com
DRUG METABOLISM
45
Rapid Metabolite Identification Using
MetabolitePilot™ Software and the TripleTOF®
5600 System
Easily find, identify, and confirm metabolites in accurate mass data
Carmai Seto, Tanya Gamble, and Hesham Ghobarah
AB SCIEX, Concord, Ontario, Canada
Key scientific challenges of metabolite ID workflows
•
•
•
High-performance, integrated software – In the past, the work of
finding, confirming, and quantifying metabolites has required the use of
multiple software packages. MetabolitePilot™ Software can perform all
three functions in one software package.
Complicated data correlation – Typically after processing quantitative
data another software package must be used to correlate data,
(e.g., interspecies comparisons, lot-to-lot variations, time
course studies).
Structural elucidation – The ability to match fragments to proposed
structures speeds the structural elucidation process, providing critical
“soft-spot” information to the medicinal chemist in a timely manner.
based on the structure of the parent compound. It also
automatically calculates mass-defect filters for phase I metabolites,
including the parent drug, as well as phase II and potential
cleavage metabolites (Figure 1).
MetabolitePilot Software makes the work of identifying and
confirming metabolites more efficient with an easy-to-use,
integrated workspace that displays all relevant information.
The software displays the analog, MS, and MS/MS data
associated with each metabolite in a single view (Figure 2).
A confirmation score is also provided to facilitiate metabolite
peak detection (Figure 3). To aid with the confirmation of
metabolite peak identification, the software enables faster
comparison of metabolite and parent product ion spectra with
a simple and effective display. The product ion spectrum of the
parent drug is automatically compared with each metabolite,
and the results of the comparison are displayed in the MS/MS
panel of the results workspace.
Materials and methods
Sample preparation
Bromocriptine was incubated in rat liver microsomes under
oxidative conditions at 37° C for 1 hr, along with a control. The
reactions were quenched by the addition of an equal volume of
acetonitrile to the microsomal solutions and then centrifuged at
14,000 rpm for 5 min.
Chromatography
The incubations were analyzed using a Shimadzu Prominence
UFLC-XR LC system with UV detector set at 302 nm. All analyses
were performed using an Imtakt Cadenza CD-C18 (2×50 mm)
3µm, ODS column that was held at 40° C. Solvent A contained
water with 0.1% formic acid and solvent B contained acetonitrile
with 0.1% formic acid, and the flow rate was set at 600 µL/min.
The gradient is summarized in Table 1.
Gradient:Time Gradient
(min)
composition (%B)
Key benefits of MetabolitePilot Software
•
•
•
•
Speed – Data can be batch-processed, saving time during analysis.
A comprehensive software package – MetabolitePilot Software
finds metabolites, speeds confirmation the site of metabolism with
fragmentation interpretation tools, and correlates data across userdefined domains, with multiple options for visualizing the data.
Structure-driven processing – Processing parameters are based on the
structure of the parent drug and can be rapidly and easily built.
Correlation with analog (UV, DAD, or radioactivity) data – Peak areas
from both mass spectrometry and analog data are both provided.
Key features of MetabolitePilot Software
•
•
•
•
Multiple methods for finding metabolites – Data can be mined for
expected and unexpected metabolites (unpredicted chromatographic
peaks), as well as by mass defect, isotope patterns and similarity of the
product ion spectra with that of the parent drug.
Data comparisons against up to five controls – For example, during GSH
screenings, the software encorporates a t0 control, a second control
without the test article, a third control without GSH, and another
control without NADPH.
Integrated MS/MS fragment interpretation and structure editing –
These features speed the determination of the site of metabolism.
Weighted scoring – Scoring may be customized and is based on mass
accuracy, isotopic pattern fidelity, similarity and quality of the MS/MS
data to that of the parent drug, and mass-defect matching.
Introduction
The ability to find, identify, and confirm metabolites as quickly
as possible is critical at multiple stages of drug discovery and
development. Advances in instrumentation have enabled the
generation of very information-rich raw data. New software
44
RUO-MKT-01-1583-A
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
programs that automate portions of the metabolite identification
workflow provide for considerable benefits to drug metabolism
researchers. For example, accurate mass instruments such as
the time-of-flight (TOF) MS have the capability to provide both
qualitative and quantitative information in a single analysis.
However, the data analysis can be quite complicated and very
time consuming if performed manually, especially if analog data
was acquired along with MS and MS/MS data. Software programs
can automate the process of finding, identifying, and confirming
metabolites in accurate mass data. This technical note describes
how MetabolitePilot Software and the AB SCIEX TripleTOF® 5600
System can make the process of identifying and confirming
metabolites more efficient than ever before.
Figure 1: Multiple mass-defect filtering setup in the processing parameters.
MetabolitePilot™ Software automatically calculates the mass-defect filters for multiple
metabolite classes using the parent structure.
DRUG METABOLISM
www.absciex.com
10
0.5
10
0.75
20
5.5
40
6.5
60
6.8
90
6.9
90
7
10
Table 1: LC gradient conditions.
Mass spectrometry
The TripleTOF 5600 system method used for data acquisition
consisted of a TOF MS survey scan followed by two IDA TOF MS/
MS scans. Mass Defect triggered IDA with Dynamic Background
Subtraction™ algorithm was used to collect the MS/MS data.
External mass calibration was performed automatically using a
calibrant delivery system.
MetabolitePilot Software: Automated identification and
confirmation of metabolites
MetabolitePilot Software employs multiple, powerful data
processing algorithms to perform both non-targeted and targeted
processing in parallel. In addition, generic peak-finding, multiple
mass-defect filtering, isotope-pattern matching, and metabolite
detection based on common product ions or neutral losses
can all be used simultaneously to find and identify metabolites
efficiently. To harness the power of these algorithms, the software
automatically calculates the most appropriate compound-specific
processing parameters for a given compound. MetabolitePilot
Software automatically predicts potential cleavage metabolites
0
Figure 2: Results workspace of MetabolitePilot™ Software. All data is displayed in a
single workspace for efficient data review.
www.absciex.com
DRUG METABOLISM
45
For Research Use Only. Not for use in diagnostic procedures.
LATE STAGE DISCOVERY
Along with the interpreted spectrum of bromocriptine, structural
elucidation of the metabolites was quickly achieved using the
excellent mass accuracies achieved by the TripleTOF 5600
system. When comparing product ion spectra, common product
ions between metabolites and parent drug (bromocriptine)
indicate where metabolism has not occurred, while common
neutral losses indicate where metabolism has occurred.
The comparison process is made more efficient by the
automated product ion comparisons and fully integrated,
automatic MS/MS fragment interpretation and assignment
performed by MetabolitePilot Software. Editing and storage
of metabolite structures can be performed directly within the
software. Interpretation and inter-sample correlation capabilities
are covered in detail in reference 3.
•
An example of the product ion spectra used for structural
elucidation in this study is shown in Figure 5; the spectrum for an
oxidation metabolite (M6), a desaturation metabolite (M23) and
bromocriptine are shown. Due to the presence of the common
product ion, m/z 426.0809, the sites of metabolites were
determined not to be localized on the substructure represented by
m/z 426.0809. There is no ambiguity in the elemental composition
of the product ion due to the mass accuracies achieved by the
TripleTOF 5600 system for this experiment.
•
Conclusions
•
•
The TripleTOF 5600 system and MetabolitePilot Software effectively
address the data analysis bottleneck in metabolite identification, from
peak detection to structural elucidation.
Efficiently find metabolites using powerful, multiple, peak-finding
strategies applied in parallel, such as non-targeted, generic peak-finding
and mass-defect filtering.
•
•
Automatic generation of structure-based processing parameters such
as multiple mass-defect ranges and potential cleavage metabolites
eases metabolite structural assignments.
Efficient processing of multiple sample sets together occurs in the
Batch Workspace.
Structural elucidation is rapidly performed using integrated
MS/MS fragment interpretation and assignment, all in a single,
user-friendly workspace.
Automatic correlation of UV, MS, and MS/MS data for multiple samples
and plotting of response across samples enables faster and easier
evaluation of the time course studies and inter-species comparison.
References
1
“Breakthrough Productivity for ADME Studies Using the AB SCIEX TripleTOF® 5600 System.”
AB SCIEX Technical Note. Publication 0480110-01.
2
“Simultaneous Pharmacokinetic Profiling and Automated Metabolite Identification Using the
AB SCIEX TripleTOF® 5600 System and MetabolitePilot™ Software.” AB SCIEX Technical Note.
Publication 1270210-01.
3
“Solving Bottlenecks in Metabolite ID Data Analysis With MetabolitePilot™ Software”, AB SCIEX
Technical Note, Publication 3610211-01
4
To download a trial version of MetabolitePilot™ Software, please visit: http://www.absciex.com/
Products/Software/MetabolitePilot-Software
LATE STAGE DISCOVERY
The identified peaks were confirmed as metabolites using the
confirmation score provided by the software, the exact mass
product ion spectra and the UV data.
Figure 3: Detailed information about a confirmation score. Each potential
metabolite is given a confirmation score to help determine if the peak is likely
to be a metabolite.
Results
MetabolitePilot Software was used to process the data acquired
from the incubation. In the results workspace, the MS and UV
data were correlated, with the potential metabolites, indicating a
corresponding analog peak for any MS peaks (Figure 2).
Figure 4 illustrates the structures proposed for some
of the metabolites that were identified by the software.
Figure 4: Proposed structures for some bromocriptine metabolites identified in a rat
microsomal incubation.
46
RUO-MKT-01-1583-A
Figure 5: Product ion spectra of two metabolites and bromocriptine. Automated
MS/MS comparisons and excellent mass accuracies allowed for the rapid structural
elucidation of metabolites.
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
47
For Research Use Only. Not for use in diagnostic procedures.
LATE STAGE DISCOVERY
Along with the interpreted spectrum of bromocriptine, structural
elucidation of the metabolites was quickly achieved using the
excellent mass accuracies achieved by the TripleTOF 5600
system. When comparing product ion spectra, common product
ions between metabolites and parent drug (bromocriptine)
indicate where metabolism has not occurred, while common
neutral losses indicate where metabolism has occurred.
The comparison process is made more efficient by the
automated product ion comparisons and fully integrated,
automatic MS/MS fragment interpretation and assignment
performed by MetabolitePilot Software. Editing and storage
of metabolite structures can be performed directly within the
software. Interpretation and inter-sample correlation capabilities
are covered in detail in reference 3.
•
An example of the product ion spectra used for structural
elucidation in this study is shown in Figure 5; the spectrum for an
oxidation metabolite (M6), a desaturation metabolite (M23) and
bromocriptine are shown. Due to the presence of the common
product ion, m/z 426.0809, the sites of metabolites were
determined not to be localized on the substructure represented by
m/z 426.0809. There is no ambiguity in the elemental composition
of the product ion due to the mass accuracies achieved by the
TripleTOF 5600 system for this experiment.
•
Conclusions
•
•
The TripleTOF 5600 system and MetabolitePilot Software effectively
address the data analysis bottleneck in metabolite identification, from
peak detection to structural elucidation.
Efficiently find metabolites using powerful, multiple, peak-finding
strategies applied in parallel, such as non-targeted, generic peak-finding
and mass-defect filtering.
•
•
Automatic generation of structure-based processing parameters such
as multiple mass-defect ranges and potential cleavage metabolites
eases metabolite structural assignments.
Efficient processing of multiple sample sets together occurs in the
Batch Workspace.
Structural elucidation is rapidly performed using integrated
MS/MS fragment interpretation and assignment, all in a single,
user-friendly workspace.
Automatic correlation of UV, MS, and MS/MS data for multiple samples
and plotting of response across samples enables faster and easier
evaluation of the time course studies and inter-species comparison.
References
1
“Breakthrough Productivity for ADME Studies Using the AB SCIEX TripleTOF® 5600 System.”
AB SCIEX Technical Note. Publication 0480110-01.
2
“Simultaneous Pharmacokinetic Profiling and Automated Metabolite Identification Using the
AB SCIEX TripleTOF® 5600 System and MetabolitePilot™ Software.” AB SCIEX Technical Note.
Publication 1270210-01.
3
“Solving Bottlenecks in Metabolite ID Data Analysis With MetabolitePilot™ Software”, AB SCIEX
Technical Note, Publication 3610211-01
4
To download a trial version of MetabolitePilot™ Software, please visit: http://www.absciex.com/
Products/Software/MetabolitePilot-Software
LATE STAGE DISCOVERY
The identified peaks were confirmed as metabolites using the
confirmation score provided by the software, the exact mass
product ion spectra and the UV data.
Figure 3: Detailed information about a confirmation score. Each potential
metabolite is given a confirmation score to help determine if the peak is likely
to be a metabolite.
Results
MetabolitePilot Software was used to process the data acquired
from the incubation. In the results workspace, the MS and UV
data were correlated, with the potential metabolites, indicating a
corresponding analog peak for any MS peaks (Figure 2).
Figure 4 illustrates the structures proposed for some
of the metabolites that were identified by the software.
Figure 4: Proposed structures for some bromocriptine metabolites identified in a rat
microsomal incubation.
46
RUO-MKT-01-1583-A
Figure 5: Product ion spectra of two metabolites and bromocriptine. Automated
MS/MS comparisons and excellent mass accuracies allowed for the rapid structural
elucidation of metabolites.
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
47
In Vivo Metabolic Profiling of Carbamazepine
Using the QTRAP® 5500 System and
LightSight® Software 2.2
Key features of the QTRAP 5500® System and LightSight
Software 2.2 for in vivo metabolitic profiling
•
•
Tanya Gamble 1, Henrianna Pang 2, Sophie Pang 2, Yingbo Yang 2, William Cui 2, Julia Izhakova 2, Douglas Turk 2, and Hesham Ghobarah 1
1
AB SCIEX, Concord, Ontario; 2NoAb BioDiscoveries Inc., Mississauga, Ontario, Canada
•
Key scientific challenges of in vivo metabolitic profiling
•
•
Matrix interferences – Extracting or finding metabolites in complex
biological matrices used for in vivo metabolic studies often requires
both sensitive and specific scan types.
Speed – High-throughput analyses, driven by the desire for
efficient data collection and reporting, enables rapid detection and
identification of metabolites in drug discovery laboratories.
Key benefits of the QTRAP® 5500 System and LightSight
Software 2.2 for in vivo metabolitic profiling
•
•
•
Ease-of-use – LightSight Software can be used to process qualitative
and quantitative data simultaneously.
High sensitivity – The high sensitivity of the QTRAP 5500 system allows
for metabolic profiling at physiologically relevant dosing.
High specificity – The unique scanning functions of the QTRAP 5500
system combine high specificity and sensitivity for powerful metabolite
ID workflows.
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
TripleTrap™ Scanning – These scanning functions unique to the
QTRAP systems combine highly specific triple quadrupole scans with
very fast and sensitive linear ion trap (LIT) scans for powerful metabolite
ID workflows.
An integrated software package – Quantitative and qualitative data
are easily and automatically generated using LightSight Software.
(Figure 1 shows processed data from a dual survey acquisition method.)
LightSight Software will also build acquisition methods and acquire
data.
Sensitivity – The high sensitivity of the QTRAP 5500 system allows it to
find more metabolites than previous systems.
Overview
This application note demonstrates the use of ultra-high pressure
liquid chromatography (UHPLC) with fast-scanning mass
spectrometric techniques for the identification of carbamazepine
and its metabolites in rat plasma. The unique, fast-scanning
capabilities of the QTRAP 5500 system couples highly specific
triple quadrupole scan modes with highly sensitive LIT product ion
scans for the rapid detection and identification of metabolites.
LightSight Software 2.2 was employed to create and submit
acquisition methods. All data processing was performed using a
single processing method in LightSight Software.
Figure 2: Experimental conditions with UHPLC gradient.
Introduction
Traditionally, in vitro and in vivo metabolic profiling has been
performed at high, non-clinically relevant doses due to the
sensitivity and selectivity limitiations of mass spectrometric
detection. A metabolic profile at these high doses can be
significantly different from the profile at physiologically relevant
concentrations. It is highly desirable to perform in vivo studies at
clinically relevant dose levels; however, these studies are often
performed at such a low concentration that significant analytical
challenges are posed during qualitative analysis.
For this study, researchers evaluated the use of UPLC coupled
to a hybrid triple quadrupole linear ion trap mass spectrometer
for metabolic profiling of carbamazepine at physiologically
relevant concentrations.
Experimental conditions
Carbamazepine was administered intravenously to SpragueDawley rats at 4 mg/kg dose. Plasma samples were collected prior
to dosing and at 15, 60, 180, and 360 min and stored at -20° C
prior to analysis. Each sample was prepared using simple protein
precipitation with 50/50 methanol/acetonitrile, centrifuged,
dried down, and reconstituted in 95/5/0.1 water/acetonitrile/
formic acid.
Figure 1: Data processed using LightSight® Software. Dual survey data from a precursor ion and neutral loss information-dependent acquisition (IDA) method processed in
LightSight Software 2.2.
48
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
Carbamazepine and its metabolites were chromatographically
separated using an Acquity UPLC system. Mass spectrometric
detection was performed using a QTRAP 5500 system using
the conditions shown in Figure 2. This unique hybrid triple
www.absciex.com
quadrupole/linear ion trap instrument couples the ability to use
highly specific triple quadrupole scan modes such as precursor
ion (Prec) or neutral loss (NL) scanning to trigger highly sensitive
linear ion trap product ion scans using information-dependent
acquisition (IDA). The acquisition method wizard in LightSight
Software 2.2 (Figure 3) was used to create all methods, including
a predictive MRM (pMRM) approach and a method that combines
a neutral loss and precursor ion scan to trigger MS/MS.
Results and discussion
Figure 4 shows the molecular structure of carbamazepine with
fragmentation sites incurred during MS/MS analysis indicated
along with the nominal mass of the fragment or neutral loss mass
(NL). The m/z 194 fragment ion was chosen as the Q3 mass for
the pMRM method, as well as the fragment for the precursor/
neutral loss method.
A two-injection strategy was used to find the majority of the
metabolites. The first injection used the pMRM IDA method and
was able to detect 18 potential metabolites. The dual precursor
ion/neutral loss IDA method ensured that unpredicted metabolites
were not missed. Screens for sulfate and glucuronide conjugation
were completed using a neutral loss scan m/z of 80 and 176 in a
third injection. In total, 20 potential metabolites were identified
with information-rich MS/MS spectra collected using the highly
sensitive ion trap.
DRUG METABOLISM
49
In Vivo Metabolic Profiling of Carbamazepine
Using the QTRAP® 5500 System and
LightSight® Software 2.2
Key features of the QTRAP 5500® System and LightSight
Software 2.2 for in vivo metabolitic profiling
•
•
Tanya Gamble 1, Henrianna Pang 2, Sophie Pang 2, Yingbo Yang 2, William Cui 2, Julia Izhakova 2, Douglas Turk 2, and Hesham Ghobarah 1
1
AB SCIEX, Concord, Ontario; 2NoAb BioDiscoveries Inc., Mississauga, Ontario, Canada
•
Key scientific challenges of in vivo metabolitic profiling
•
•
Matrix interferences – Extracting or finding metabolites in complex
biological matrices used for in vivo metabolic studies often requires
both sensitive and specific scan types.
Speed – High-throughput analyses, driven by the desire for
efficient data collection and reporting, enables rapid detection and
identification of metabolites in drug discovery laboratories.
Key benefits of the QTRAP® 5500 System and LightSight
Software 2.2 for in vivo metabolitic profiling
•
•
•
Ease-of-use – LightSight Software can be used to process qualitative
and quantitative data simultaneously.
High sensitivity – The high sensitivity of the QTRAP 5500 system allows
for metabolic profiling at physiologically relevant dosing.
High specificity – The unique scanning functions of the QTRAP 5500
system combine high specificity and sensitivity for powerful metabolite
ID workflows.
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
TripleTrap™ Scanning – These scanning functions unique to the
QTRAP systems combine highly specific triple quadrupole scans with
very fast and sensitive linear ion trap (LIT) scans for powerful metabolite
ID workflows.
An integrated software package – Quantitative and qualitative data
are easily and automatically generated using LightSight Software.
(Figure 1 shows processed data from a dual survey acquisition method.)
LightSight Software will also build acquisition methods and acquire
data.
Sensitivity – The high sensitivity of the QTRAP 5500 system allows it to
find more metabolites than previous systems.
Overview
This application note demonstrates the use of ultra-high pressure
liquid chromatography (UHPLC) with fast-scanning mass
spectrometric techniques for the identification of carbamazepine
and its metabolites in rat plasma. The unique, fast-scanning
capabilities of the QTRAP 5500 system couples highly specific
triple quadrupole scan modes with highly sensitive LIT product ion
scans for the rapid detection and identification of metabolites.
LightSight Software 2.2 was employed to create and submit
acquisition methods. All data processing was performed using a
single processing method in LightSight Software.
Figure 2: Experimental conditions with UHPLC gradient.
Introduction
Traditionally, in vitro and in vivo metabolic profiling has been
performed at high, non-clinically relevant doses due to the
sensitivity and selectivity limitiations of mass spectrometric
detection. A metabolic profile at these high doses can be
significantly different from the profile at physiologically relevant
concentrations. It is highly desirable to perform in vivo studies at
clinically relevant dose levels; however, these studies are often
performed at such a low concentration that significant analytical
challenges are posed during qualitative analysis.
For this study, researchers evaluated the use of UPLC coupled
to a hybrid triple quadrupole linear ion trap mass spectrometer
for metabolic profiling of carbamazepine at physiologically
relevant concentrations.
Experimental conditions
Carbamazepine was administered intravenously to SpragueDawley rats at 4 mg/kg dose. Plasma samples were collected prior
to dosing and at 15, 60, 180, and 360 min and stored at -20° C
prior to analysis. Each sample was prepared using simple protein
precipitation with 50/50 methanol/acetonitrile, centrifuged,
dried down, and reconstituted in 95/5/0.1 water/acetonitrile/
formic acid.
Figure 1: Data processed using LightSight® Software. Dual survey data from a precursor ion and neutral loss information-dependent acquisition (IDA) method processed in
LightSight Software 2.2.
48
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
Carbamazepine and its metabolites were chromatographically
separated using an Acquity UPLC system. Mass spectrometric
detection was performed using a QTRAP 5500 system using
the conditions shown in Figure 2. This unique hybrid triple
www.absciex.com
quadrupole/linear ion trap instrument couples the ability to use
highly specific triple quadrupole scan modes such as precursor
ion (Prec) or neutral loss (NL) scanning to trigger highly sensitive
linear ion trap product ion scans using information-dependent
acquisition (IDA). The acquisition method wizard in LightSight
Software 2.2 (Figure 3) was used to create all methods, including
a predictive MRM (pMRM) approach and a method that combines
a neutral loss and precursor ion scan to trigger MS/MS.
Results and discussion
Figure 4 shows the molecular structure of carbamazepine with
fragmentation sites incurred during MS/MS analysis indicated
along with the nominal mass of the fragment or neutral loss mass
(NL). The m/z 194 fragment ion was chosen as the Q3 mass for
the pMRM method, as well as the fragment for the precursor/
neutral loss method.
A two-injection strategy was used to find the majority of the
metabolites. The first injection used the pMRM IDA method and
was able to detect 18 potential metabolites. The dual precursor
ion/neutral loss IDA method ensured that unpredicted metabolites
were not missed. Screens for sulfate and glucuronide conjugation
were completed using a neutral loss scan m/z of 80 and 176 in a
third injection. In total, 20 potential metabolites were identified
with information-rich MS/MS spectra collected using the highly
sensitive ion trap.
DRUG METABOLISM
49
For Research Use Only. Not for use in diagnostic procedures.
LATE STAGE DISCOVERY
•
•
Figure 4: Carbamazepine structure. The portion of the molecule corresponding to
the m/z 194 fragment used for precursor scans is shown in blue, and the m/z 43
fragment used for neutral loss scans is shown in orange.
•
•
•
Figure 3: The acquisition method wizard in LightSight Software 2.2.
A single processing method was created to detect metabolites
in all scan modes explored (Figure 5). The identities of several
of the metabolites were confirmed using standards. Figure 6
illustrates the metabolite separation achieved using UHPLC.
Table 1 summarizes the potential metabolites detected.
Confirmatory enhance product ion (EPI) spectra were obtained
for all species.
The fast-scanning capabilities of the QTRAP 5500 system
enable the coupling of specific and sensitive triple quadrupole
Figure 5: The processing method for carbamazepine metabolite data. A single
processing method can be used for all scan types needed for metabolic profiling on
QTRAP systems and includes scan-type specific parameters.
50
RUO-MKT-01-1583-A
Metabolic profiling of carbamazepine was performed using LightSight
Software 2.2 and the QTRAP 5500 system.
LightSight Software 2.2 accelerates metabolic profiling by:
• Creating optimized acquisition methods for metabolite detection.
• Automatically creating IDA acquisition methods with two survey
scans for the QTRAP 5500 system.
• Automatically creating glucuronide neutral loss methods.
• Creating and submitting acquisition methods using the Acquity®
UHPLC system.
• Allowing users to create and save processing methods.
Use of a single processing method can be used for data reduction and
identification of metabolites from dual-survey scan data acquisition.
A dual-survey scan approach combining neutral loss and precursor ions
scans provides complementary information to the predictive MRM IDA
approach.
The high speed and sensitivity of the QTRAP 5500 system allows
for metabolic-profiling studies at more physiologically relevant
concentrations.
LATE STAGE DISCOVERY
Conclusions
scan modes (Prec/NL/MRM) with highly sensitive product ion scans
on a UHPLC time-scale. The use of quadrupole mode survey scans
can be especially useful for sensitive determination of low-level
metabolites in complex matrices. The ability to combine multiple
survey scans is an extremely powerful solution for the targeted
detection of phase II metabolites such as glucuronide
and glutathione conjugates. The aforementioned strategies
for detecting metabolites are fully enabled by LightSight
Software 2.2.
Table 1: Potential metabolites of carbamazepine and their proposed structures (RT = retention time in min).
Figure 6: UHPLC separation of carbamazepine metabolites.
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
51
For Research Use Only. Not for use in diagnostic procedures.
LATE STAGE DISCOVERY
•
•
Figure 4: Carbamazepine structure. The portion of the molecule corresponding to
the m/z 194 fragment used for precursor scans is shown in blue, and the m/z 43
fragment used for neutral loss scans is shown in orange.
•
•
•
Figure 3: The acquisition method wizard in LightSight Software 2.2.
A single processing method was created to detect metabolites
in all scan modes explored (Figure 5). The identities of several
of the metabolites were confirmed using standards. Figure 6
illustrates the metabolite separation achieved using UHPLC.
Table 1 summarizes the potential metabolites detected.
Confirmatory enhance product ion (EPI) spectra were obtained
for all species.
The fast-scanning capabilities of the QTRAP 5500 system
enable the coupling of specific and sensitive triple quadrupole
Figure 5: The processing method for carbamazepine metabolite data. A single
processing method can be used for all scan types needed for metabolic profiling on
QTRAP systems and includes scan-type specific parameters.
50
RUO-MKT-01-1583-A
Metabolic profiling of carbamazepine was performed using LightSight
Software 2.2 and the QTRAP 5500 system.
LightSight Software 2.2 accelerates metabolic profiling by:
• Creating optimized acquisition methods for metabolite detection.
• Automatically creating IDA acquisition methods with two survey
scans for the QTRAP 5500 system.
• Automatically creating glucuronide neutral loss methods.
• Creating and submitting acquisition methods using the Acquity®
UHPLC system.
• Allowing users to create and save processing methods.
Use of a single processing method can be used for data reduction and
identification of metabolites from dual-survey scan data acquisition.
A dual-survey scan approach combining neutral loss and precursor ions
scans provides complementary information to the predictive MRM IDA
approach.
The high speed and sensitivity of the QTRAP 5500 system allows
for metabolic-profiling studies at more physiologically relevant
concentrations.
LATE STAGE DISCOVERY
Conclusions
scan modes (Prec/NL/MRM) with highly sensitive product ion scans
on a UHPLC time-scale. The use of quadrupole mode survey scans
can be especially useful for sensitive determination of low-level
metabolites in complex matrices. The ability to combine multiple
survey scans is an extremely powerful solution for the targeted
detection of phase II metabolites such as glucuronide
and glutathione conjugates. The aforementioned strategies
for detecting metabolites are fully enabled by LightSight
Software 2.2.
Table 1: Potential metabolites of carbamazepine and their proposed structures (RT = retention time in min).
Figure 6: UHPLC separation of carbamazepine metabolites.
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
51
Comprehensive Detection of Metabolites Using
Polarity Switching Data Collection with the
QTRAP® 5500 LC/MS/MS System
Carmai Seto
AB SCIEX, Concord, Ontario, Canada
Key scientific challenges of metabolite ID
•
•
•
Thoroughness – In the early stages of drug discovery and development,
it is important to find as many metabolites as possible. To eliminate
unviable candidates from the pipeline.
Restricted sample availability – In some metabolism studies, the
supply of sample for analysis may be limited. Acquiringe data in one
injection rather than two means using 50% less of an irreplaceable,
precious sample.
Productivity – Increasing productivity in drug discovery and development
continues to be a primary goal in pharmaceutical research.
Key benefits of the QTRAP® 5500 System for metabolite ID
•
•
•
High sensitivity – The QTRAP 5500 system provides high levels of
sensitivity for multiple reaction monitoring (MRM) analysis.
Rapid polarity switching – Collecting MS data in both positive and
negative ion modes while doing chromatography with narrow peaks is
critical for throughput.
Fast scanning – Trap scan speeds of up to 20,000 Da/s are available for
rapid analyses.
Key features of QTRAP 5500 systems for metabolite ID
•
Unique Workflows – workflows such as positive/negative MRMtriggered information-dependent acquisition (IDA) are supported on
the QTRAP 5500 system.
•
Ease-of-use – LightSight® Software can be used to build acquisition
methods and process the data.
Positive/negative workflows – More metabolites of varying polarities.
Speed – The QTRAP 5500 system is fully compatible with UHPLC,
providing better throughput and sensitivity for low-level metabolites.
•
•
52
RUO-MKT-01-1583-A
Experimental conditions
In discovery stage pharmaceutical research, information about
the extent of metabolism and metabolite structures is used
in lead optimization. Identified metabolites can be a source
of new therapeutics, toxicity assessment and drug soft spot
optimization. LC/MS is widely used for the identification of
metabolites at all stages of drug development. Metabolites
most often ionize with the same polarity as the parent
compound of interest. However, there are cases where this
assumption does not hold true thus leading to cases of
overlooked metabolites. Polarity switching data collection
ensures a more comprehensive way of detecting metabolites
of opposite polarities.
Simvastatin was incubated in rabbit microsomes at a
concentration of 10 µM under oxidative conditions using an
NADPH regenerating system. The incubation was quenched with
acetonitrile at time zero and 60 min and then centrifuged to pellet
the protein. The supernatant was then diluted three-fold with
water. A QTRAP 5500 LC/MS/MS system was used for acquisition
and LightSight Software for metabolite identification, version 2.1,
was used for data interpretation.
There are two approaches to collecting polarity switching
data; 1) single injection polarity switching and 2) the two
injection discrete polarity analysis (one injection in positive
and another in negative ion mode). The one injection
approach is where the positive and negative ion survey
experiments are looped throughout the run with or without
dependent IDA. In the two injection approach, the positive
and negative ion experiments are in two separate runs. This
approach, therefore, takes twice as long to obtain complete
data, and LC retention time shifts between samples could
complicate data analysis. Furthermore, greater sample
amounts are required for a two-injection workflow.
Polarity switching data collection can be achieved using various
survey scans, such as enhanced mass spectra (EMS) (trap single
MS), or quadrupole MS/MS modes such as precursor ion, neutral
loss, or MRM. For this study, a pMRM method was used. With
prior knowledge of the parent drug and its fragmentation
pathway, theoretical MRM transitions for metabolites can be
determined based on a particular biotransformation list. For this
experiment, two pMRM methods (one positive and one negative
ion) were combined into a single experiment. Each of the pMRM
surveys contained a total of 48 MRM transitions that covered
the range of phase I metabolites. MS/MS data was collected
automatically in negative ion via IDA at a scan rate of
10,000 Da/sec. The structure of the method is shown in
Figure 1. The overall duty cycle time for this method was
approximately 2.6 sec.
In this work, metabolites of simvastatin were detected and
confirmed in an in vitro incubation: using a single injection
polarity switching IDA methodology.
Overview
The majority of xenobiotic metabolites tend to ionize in the
same polarity as the parent drug. Typically, the majority of
therapeutics and their downstream metabolites well ionize in
positive electrospray ionization (ESI) to provide excellent sensitivity.
However, some phase II conjugates, such as phosphate or
sulfate additions, completely change the ionization character
of the molecule, switching the polarity to negative ESI. Polarity
switching data collection ensures the comprehensive detection
of metabolites in both polarities within a single LC/MS run.
Furthermore, for GSH-conjugate detection, a number of positive/
negative precursor/neutral loss or MRM IDA experiments provide
effective coverage for all possible formations1. The QTRAP 5500
system is especially effective at positive/negative IDA methods.
The system’s ability to perform fast quadrupole and linear ion trap
(LIT) scanning (a 4-fold increase in quadrupole scanning and a
5-fold increase in LIT scanning over the 4000 QTRAP® System),
50 ms polarity switching, and enhanced sensitivity in both
quadrupole and LIT mode allow for efficient metabolic detection.
These groundbreaking workflows are further optimized by the
addition of LightSight Software 2.1 for automated data processing
and IDA method creation.
DRUG METABOLISM
Introduction
www.absciex.com
5% A – 85% A in 12.5 min 85% - 95% A in 1 min 95% A
isocratic for 1.5 min A is 0.1% formic acid in acetonitrile and B is
0.1% formic acid in water. A 400 uL/min gradient was used with
a Phenomenex Luna C18(2) 3 µm, 50 × 2 mm HPLC column.
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Figure 1: IDA method options. The polarity switching IDA method shown here was
used to detect metabolites of simvastatin.
www.absciex.com
DRUG METABOLISM
53
Comprehensive Detection of Metabolites Using
Polarity Switching Data Collection with the
QTRAP® 5500 LC/MS/MS System
Carmai Seto
AB SCIEX, Concord, Ontario, Canada
Key scientific challenges of metabolite ID
•
•
•
Thoroughness – In the early stages of drug discovery and development,
it is important to find as many metabolites as possible. To eliminate
unviable candidates from the pipeline.
Restricted sample availability – In some metabolism studies, the
supply of sample for analysis may be limited. Acquiringe data in one
injection rather than two means using 50% less of an irreplaceable,
precious sample.
Productivity – Increasing productivity in drug discovery and development
continues to be a primary goal in pharmaceutical research.
Key benefits of the QTRAP® 5500 System for metabolite ID
•
•
•
High sensitivity – The QTRAP 5500 system provides high levels of
sensitivity for multiple reaction monitoring (MRM) analysis.
Rapid polarity switching – Collecting MS data in both positive and
negative ion modes while doing chromatography with narrow peaks is
critical for throughput.
Fast scanning – Trap scan speeds of up to 20,000 Da/s are available for
rapid analyses.
Key features of QTRAP 5500 systems for metabolite ID
•
Unique Workflows – workflows such as positive/negative MRMtriggered information-dependent acquisition (IDA) are supported on
the QTRAP 5500 system.
•
Ease-of-use – LightSight® Software can be used to build acquisition
methods and process the data.
Positive/negative workflows – More metabolites of varying polarities.
Speed – The QTRAP 5500 system is fully compatible with UHPLC,
providing better throughput and sensitivity for low-level metabolites.
•
•
52
RUO-MKT-01-1583-A
Experimental conditions
In discovery stage pharmaceutical research, information about
the extent of metabolism and metabolite structures is used
in lead optimization. Identified metabolites can be a source
of new therapeutics, toxicity assessment and drug soft spot
optimization. LC/MS is widely used for the identification of
metabolites at all stages of drug development. Metabolites
most often ionize with the same polarity as the parent
compound of interest. However, there are cases where this
assumption does not hold true thus leading to cases of
overlooked metabolites. Polarity switching data collection
ensures a more comprehensive way of detecting metabolites
of opposite polarities.
Simvastatin was incubated in rabbit microsomes at a
concentration of 10 µM under oxidative conditions using an
NADPH regenerating system. The incubation was quenched with
acetonitrile at time zero and 60 min and then centrifuged to pellet
the protein. The supernatant was then diluted three-fold with
water. A QTRAP 5500 LC/MS/MS system was used for acquisition
and LightSight Software for metabolite identification, version 2.1,
was used for data interpretation.
There are two approaches to collecting polarity switching
data; 1) single injection polarity switching and 2) the two
injection discrete polarity analysis (one injection in positive
and another in negative ion mode). The one injection
approach is where the positive and negative ion survey
experiments are looped throughout the run with or without
dependent IDA. In the two injection approach, the positive
and negative ion experiments are in two separate runs. This
approach, therefore, takes twice as long to obtain complete
data, and LC retention time shifts between samples could
complicate data analysis. Furthermore, greater sample
amounts are required for a two-injection workflow.
Polarity switching data collection can be achieved using various
survey scans, such as enhanced mass spectra (EMS) (trap single
MS), or quadrupole MS/MS modes such as precursor ion, neutral
loss, or MRM. For this study, a pMRM method was used. With
prior knowledge of the parent drug and its fragmentation
pathway, theoretical MRM transitions for metabolites can be
determined based on a particular biotransformation list. For this
experiment, two pMRM methods (one positive and one negative
ion) were combined into a single experiment. Each of the pMRM
surveys contained a total of 48 MRM transitions that covered
the range of phase I metabolites. MS/MS data was collected
automatically in negative ion via IDA at a scan rate of
10,000 Da/sec. The structure of the method is shown in
Figure 1. The overall duty cycle time for this method was
approximately 2.6 sec.
In this work, metabolites of simvastatin were detected and
confirmed in an in vitro incubation: using a single injection
polarity switching IDA methodology.
Overview
The majority of xenobiotic metabolites tend to ionize in the
same polarity as the parent drug. Typically, the majority of
therapeutics and their downstream metabolites well ionize in
positive electrospray ionization (ESI) to provide excellent sensitivity.
However, some phase II conjugates, such as phosphate or
sulfate additions, completely change the ionization character
of the molecule, switching the polarity to negative ESI. Polarity
switching data collection ensures the comprehensive detection
of metabolites in both polarities within a single LC/MS run.
Furthermore, for GSH-conjugate detection, a number of positive/
negative precursor/neutral loss or MRM IDA experiments provide
effective coverage for all possible formations1. The QTRAP 5500
system is especially effective at positive/negative IDA methods.
The system’s ability to perform fast quadrupole and linear ion trap
(LIT) scanning (a 4-fold increase in quadrupole scanning and a
5-fold increase in LIT scanning over the 4000 QTRAP® System),
50 ms polarity switching, and enhanced sensitivity in both
quadrupole and LIT mode allow for efficient metabolic detection.
These groundbreaking workflows are further optimized by the
addition of LightSight Software 2.1 for automated data processing
and IDA method creation.
DRUG METABOLISM
Introduction
www.absciex.com
5% A – 85% A in 12.5 min 85% - 95% A in 1 min 95% A
isocratic for 1.5 min A is 0.1% formic acid in acetonitrile and B is
0.1% formic acid in water. A 400 uL/min gradient was used with
a Phenomenex Luna C18(2) 3 µm, 50 × 2 mm HPLC column.
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Figure 1: IDA method options. The polarity switching IDA method shown here was
used to detect metabolites of simvastatin.
www.absciex.com
DRUG METABOLISM
53
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Figure 2: Structures of simvastatin and simvastatin hydroxyl acid.
Results and discussion
Simvastatin is a lactone prodrug and is hydrolyzed to the active
compound (simvastatin hydroxy acid, Figure 2). Both simvastatin
and the hydroxy acid ionize in positive ion mode. However, if the
MS/MS acquisition method was only in positive ion mode, some
metabolites may not be detected because the carboxylic acid
moiety ionizes predominately in the negative mode (Figure 3).
Since both simvastatin and its carboxylic acid form can be
metabolized, it is important to monitor both polarities. Previously,
it was not possible to collect data in both polarities as well as
confirmatory information in a single injection due to IDA cycle
time limitations. However, with improvements to the polarity
switching capabilities (50 ms) of the QTRAP 5500 system
combined with the fast trap and quadrupole scanning of the new
system; many single and dual survey pos/neg IDA workflows are
now possible.
Proposed structures of some of the metabolites detected are
given in Figure 4; some were metabolites of simvastatin, while
the majority of those metabolites detected were modifications
to the hydroxy acid. Using LightSight Software, a total of 34
metabolites were detected in the microsomal incubation using
automated data processing. The metabolites detected in both
polarities were correlated in one session (Figure 5), making
Figure 3: Pos/Neg switching. The detection of simvastatin carboxylic acid in both
positive (top) and negative ion (bottom); the compound is better detected using
negative ion mode.
reporting of results easier. MS/MS data was collected in both
positive ion and negative ion modes, where the data in the
negative polarity was collected automatically through IDA,
and the data in the positive polarity was collected through
multi-experiment, period-based MS/MS methods.
Figure 4: Metabolites of simvastatin detected. Metabolites of simvastatin and simvastatin hydroxy acid identified in a rabbit microsomal incubation.
Of the 34 metabolites, 5 metabolites were only detected in
positive ion, 28 metabolites were detected in negative ion and 1
metabolite (Mx) was detected in both polarities. An advantage of
detecting metabolites in both polarities is that MS/MS data can be
collected in both polarities, which provides more information for
structural elucidation. The MS/MS spectra of Mx are depicted in
Figure 6. If the MS/MS spectra was only collected in negative ion
Figure 6 (top), the site of oxidation could only be localized to one
half of the molecule. However, the positive ion MS/MS spectrum
provided enough additional structural information so that the site
of oxidation could be further pinpointed to the core ring structure.
Figure 5: Polarity switching data processing. An oxidative metabolite of simvastatin hydroxy acid was found using polarity switching data collection. The LightSight Software
allows this data to be processed in the same session.
54
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
55
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Figure 2: Structures of simvastatin and simvastatin hydroxyl acid.
Results and discussion
Simvastatin is a lactone prodrug and is hydrolyzed to the active
compound (simvastatin hydroxy acid, Figure 2). Both simvastatin
and the hydroxy acid ionize in positive ion mode. However, if the
MS/MS acquisition method was only in positive ion mode, some
metabolites may not be detected because the carboxylic acid
moiety ionizes predominately in the negative mode (Figure 3).
Since both simvastatin and its carboxylic acid form can be
metabolized, it is important to monitor both polarities. Previously,
it was not possible to collect data in both polarities as well as
confirmatory information in a single injection due to IDA cycle
time limitations. However, with improvements to the polarity
switching capabilities (50 ms) of the QTRAP 5500 system
combined with the fast trap and quadrupole scanning of the new
system; many single and dual survey pos/neg IDA workflows are
now possible.
Proposed structures of some of the metabolites detected are
given in Figure 4; some were metabolites of simvastatin, while
the majority of those metabolites detected were modifications
to the hydroxy acid. Using LightSight Software, a total of 34
metabolites were detected in the microsomal incubation using
automated data processing. The metabolites detected in both
polarities were correlated in one session (Figure 5), making
Figure 3: Pos/Neg switching. The detection of simvastatin carboxylic acid in both
positive (top) and negative ion (bottom); the compound is better detected using
negative ion mode.
reporting of results easier. MS/MS data was collected in both
positive ion and negative ion modes, where the data in the
negative polarity was collected automatically through IDA,
and the data in the positive polarity was collected through
multi-experiment, period-based MS/MS methods.
Figure 4: Metabolites of simvastatin detected. Metabolites of simvastatin and simvastatin hydroxy acid identified in a rabbit microsomal incubation.
Of the 34 metabolites, 5 metabolites were only detected in
positive ion, 28 metabolites were detected in negative ion and 1
metabolite (Mx) was detected in both polarities. An advantage of
detecting metabolites in both polarities is that MS/MS data can be
collected in both polarities, which provides more information for
structural elucidation. The MS/MS spectra of Mx are depicted in
Figure 6. If the MS/MS spectra was only collected in negative ion
Figure 6 (top), the site of oxidation could only be localized to one
half of the molecule. However, the positive ion MS/MS spectrum
provided enough additional structural information so that the site
of oxidation could be further pinpointed to the core ring structure.
Figure 5: Polarity switching data processing. An oxidative metabolite of simvastatin hydroxy acid was found using polarity switching data collection. The LightSight Software
allows this data to be processed in the same session.
54
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
55
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Figure 6: Structural elucidation. The MS/MS spectra of Mx (oxidation of simvastatin hydroxy acid) in positive (bottom) and negative (top) ion modes is shown. The site of
oxidation could be better pinpointed when using both spectra.
56
Summary
Highlights
Single-injection polarity switching IDA experiments on the QTRAP
5500 system provide improved metabolite detection compared
to two-injection single polarity experiments. There are four key
factors which support this conclusion. One, a single-injection
workflow requires less sample, thus allowing for a large injection
volume and improved sensitivity for a single analysis. Two, data
reduction is made easier, because only one LC/MS experiment is
required to be processed. Three, combined positive and negative
ESI metabolite profiles allow for complete confirmation of varied
species in a single analysis. Four, the fast-scanning attributes in
quadrupole and LIT modes of the QTRAP 5500 system along with
fast polarity switching capabilities allow for higher throughput
analyses by incorporating faster LC strategies.
•
RUO-MKT-01-1583-A
•
•
•
•
•
•
Improved quadrupole sensitivity (MRM response and signal-to-noise
ratio, PI, and NL)
Sensitivity improvement in trap modes (EPI, 10-100-fold)
Four times faster triple quadrupole scanning
Faster polarity switching (50 ms)
Five times faster LIT scanning (20,000 amu/sec)
Shorter fill time capabilities (50 μs)
Unique workflows ideal for metabolite identification
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
57
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Figure 6: Structural elucidation. The MS/MS spectra of Mx (oxidation of simvastatin hydroxy acid) in positive (bottom) and negative (top) ion modes is shown. The site of
oxidation could be better pinpointed when using both spectra.
56
Summary
Highlights
Single-injection polarity switching IDA experiments on the QTRAP
5500 system provide improved metabolite detection compared
to two-injection single polarity experiments. There are four key
factors which support this conclusion. One, a single-injection
workflow requires less sample, thus allowing for a large injection
volume and improved sensitivity for a single analysis. Two, data
reduction is made easier, because only one LC/MS experiment is
required to be processed. Three, combined positive and negative
ESI metabolite profiles allow for complete confirmation of varied
species in a single analysis. Four, the fast-scanning attributes in
quadrupole and LIT modes of the QTRAP 5500 system along with
fast polarity switching capabilities allow for higher throughput
analyses by incorporating faster LC strategies.
•
RUO-MKT-01-1583-A
•
•
•
•
•
•
Improved quadrupole sensitivity (MRM response and signal-to-noise
ratio, PI, and NL)
Sensitivity improvement in trap modes (EPI, 10-100-fold)
Four times faster triple quadrupole scanning
Faster polarity switching (50 ms)
Five times faster LIT scanning (20,000 amu/sec)
Shorter fill time capabilities (50 μs)
Unique workflows ideal for metabolite identification
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
57
Simultaneous Pharmacokinetic Profiling and
Automated Metabolite Identification Using
the AB SCIEX TripleTOF® 5600 System and
MetabolitePilot™ Software
The AB SCIEX TripleTOF 5600 System is a state-of-the-art
high-resolution accurate mass instrument possessing the
speed, sensitivity, and linearity to deliver triple-quadrupolelike quantitative performance and accurate mass metabolite
identification in the same injection and at the same time.
Suma Ramagiri, Jeff Miller, and Hesham Ghobarah
Materials and methods
AB SCIEX, Toronto, Ontario and Framingham, Massachusetts
This application note describes the use of the TripleTOF
5600 system for simultaneous in vivo PK quantification and
identification of the major metabolites of buspirone following
administration in rat using the workflow in Figure 1.
In vivo administration
Three Sprague-Dawley rats were dosed with buspirone at
10 mg/kg intravenously. Plasma samples were collected at 0, 15,
30, 60, 120, 240, 360, and 480 min.
Key scientific challenges of combined quant and
qual workflows
Instrumentation – Prior to the debut of the TripleTOF® 5600 System,
obtaining accurate quantification and high-quality qualitative
information required using a triple quad mass spectrometer for quant
and an accurate mass instrument for qual.
•
Analysis at relevant doses – For many compounds, ADME studies were
previously conducted at a higher, non-clinically relevant concentrations
to monitor drug metabolism, occasionally leading to false conclusions as
the metabolic pathway has been found to be concentration-dependent
metabolic pathways for some compounds.
Productivity – The ability to combine quantitative bioanalysis and in vivo
metabolite ID into one analysis will increase productivity in both drug
discovery and development.
Protein was precipitated from plasma samples using acetonitrile
containing an internal standard (buspirone-d8) at a 3:1 ratio
followed by centrifugation at 10,000 x g. The supernatant (100
µL) was mixed with 50 µL water, transferred into autosampler
vials, and injected.
•
•
Quant and qual in one run – The sensitivity of TripleTOF systems on par
with the API 4000™ System, which are known throughout the industry
for their sensitivity and robustness. In the same instrument, the high
performance accurate mass analyzer allows for both quantitative and
qualitative analyses in a single method.
Intelligent data acquisition – Real-time multiple mass-defect filtering
(MDF) for information-dependent acquisition (IDA) increases workflow
efficiency by acquiring relevant MS/MS data in every run, eliminating the
need for re-injection in many cases.
Speed – Both drug discovery and development labs are standardizing
on ultra-high pressure liquid chromatography (UHPLC) as the separation
method of choice. UHPLC’s narrower chromatographic peaks result in
higher concentrations at peak apices and, therefore, lower LOQs. To
accommodate the UHPLC time scale, mass spectrometers must perform
the needed experiments on a short time scale. The TripleTOF systems are
designed to keep up with UHPLC peaks.
Key features of TripleTOF systems for
quant/qual workflows
•
58
Resolution and mass accuracy regardless of scan speed – Instruments
employing orbital trapping require slower scanning to get higher
resolution. The TripleTOF system maintains approximately 30K resolution
regardless of analysis speed.
RUO-MKT-01-1583-A
•
•
Resolution and mass accuracy even at low m/z – Traditional time-offlight (TOF) instruments provided good mass accuracy and resolution at
high m/z. The high detector speed of the TripleTOF 5600 system allows
for sufficient resolution at low m/z, providing unambiguous formulae
ssignments when using accurate mass information, as well as the
isotope and fragmentation pattern.
Integrated software packages – To date, no manufacturer has provided
one software package capable of finding metabolites, confirming
sites of metabolism, tracking time course experiments, and doing
inter-species comparisons. MetabolitePilot Software does all these.
DRUG METABOLISM
www.absciex.com
The system was operated in positive electrospray mode using a
DuoSpray™ Ion Source. The information-dependent acquisition
(IDA) method consisted of a TOF MS survey scan (m/z 100–1000)
followed by three TOF MS/MS dependent scans (m/z 50–1000).
The TOF MS scan data was used for quantification, while the
MS/MS data was used for the accurate mass structure elucidation
of detected metabolites.
Mass defect filtering (MDF) has been shown to be a powerful
tool in detecting metabolites that are similar in elemental
composition to the parent. Traditionally, MDF is a two-step
process, where full-scan data is acquired first, then MDF analysis
is performed post-processing to identify peaks of interest. A
second injection is then performed to acquire the MS/MS spectra
of these potential metabolites.
Parent metabolite quantification
The high-speed TOF MS survey scan was used for the dual
purpose of parent compound quantification and detection of
potential metabolites. Using real-time MDF to increase IDA
efficiency, accurate mass product ion spectra were triggered
on potential metabolites and used for structural elucidation.
Quantitative PK data for buspirone parent compounds were easily
processed in MultiQuant™ Software (Figure 3).
Introduction
Combining quantitative bioanalysis and in vivo metabolite
identification (quant/qual) in the same workflow has the potential
to significantly increase the productivity of pharmaceutical drug
discovery and development. Traditionally, quantitative bioanalysis
is performed on samples obtained from pharmacokinetic (PK)
studies, while metabolite identification is done in a separate
analysis often on two different instruments. Due to sensitivity
limitations of traditional LC/MS/MS instruments, studies for
metabolite identification are often performed at much higher,
non-physiologically relevant doses than for PK studies.
Mass spectrometry
Using real-time mass defect filtering to trigger IDA, the TripleTOF
5600 system can acquire both MS and MS/MS data in the same
run, eliminating the need for a second injection. Multiple mass
defect ranges are often required to detect different metabolite
classes. The system is capable of applying multiple filters at the
same time (Figure 2). The software automatically calculates the
mass defects based on the elemental compositions provided and
performs intelligent, priority-based MS/MS triggering. This gives
priority to peaks matching the mass defect range for MS/MS data
acquisition so that both expected and unexpected metabolites
can be detected.
Key benefits of the TripleTOF 5600 system for
quant/qual workflows
•
Sample analysis was performed on the TripleTOF 5600 system
coupled to an Acquity UPLC system. An acetonitrile/water /0.1%
formic acid gradient was used on a Waters BEH C18, 2.1×100
mm, 1.7 μm column. The flow rate was 0.6 mL/min,
and the column was heated to 50°C.
Real-time multiple mass defect filter
Sample preparation
•
•
Chromatography
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Figure 1: Automated pharmacokinetic (PK) profiling and metabolite identification
using the AB SCIEX TripleTOF® 5600 System with MetabolitePilot™ Software and
MultiQuant™ Software packages.
www.absciex.com
DRUG METABOLISM
59
Simultaneous Pharmacokinetic Profiling and
Automated Metabolite Identification Using
the AB SCIEX TripleTOF® 5600 System and
MetabolitePilot™ Software
The AB SCIEX TripleTOF 5600 System is a state-of-the-art
high-resolution accurate mass instrument possessing the
speed, sensitivity, and linearity to deliver triple-quadrupolelike quantitative performance and accurate mass metabolite
identification in the same injection and at the same time.
Suma Ramagiri, Jeff Miller, and Hesham Ghobarah
Materials and methods
AB SCIEX, Toronto, Ontario and Framingham, Massachusetts
This application note describes the use of the TripleTOF
5600 system for simultaneous in vivo PK quantification and
identification of the major metabolites of buspirone following
administration in rat using the workflow in Figure 1.
In vivo administration
Three Sprague-Dawley rats were dosed with buspirone at
10 mg/kg intravenously. Plasma samples were collected at 0, 15,
30, 60, 120, 240, 360, and 480 min.
Key scientific challenges of combined quant and
qual workflows
Instrumentation – Prior to the debut of the TripleTOF® 5600 System,
obtaining accurate quantification and high-quality qualitative
information required using a triple quad mass spectrometer for quant
and an accurate mass instrument for qual.
•
Analysis at relevant doses – For many compounds, ADME studies were
previously conducted at a higher, non-clinically relevant concentrations
to monitor drug metabolism, occasionally leading to false conclusions as
the metabolic pathway has been found to be concentration-dependent
metabolic pathways for some compounds.
Productivity – The ability to combine quantitative bioanalysis and in vivo
metabolite ID into one analysis will increase productivity in both drug
discovery and development.
Protein was precipitated from plasma samples using acetonitrile
containing an internal standard (buspirone-d8) at a 3:1 ratio
followed by centrifugation at 10,000 x g. The supernatant (100
µL) was mixed with 50 µL water, transferred into autosampler
vials, and injected.
•
•
Quant and qual in one run – The sensitivity of TripleTOF systems on par
with the API 4000™ System, which are known throughout the industry
for their sensitivity and robustness. In the same instrument, the high
performance accurate mass analyzer allows for both quantitative and
qualitative analyses in a single method.
Intelligent data acquisition – Real-time multiple mass-defect filtering
(MDF) for information-dependent acquisition (IDA) increases workflow
efficiency by acquiring relevant MS/MS data in every run, eliminating the
need for re-injection in many cases.
Speed – Both drug discovery and development labs are standardizing
on ultra-high pressure liquid chromatography (UHPLC) as the separation
method of choice. UHPLC’s narrower chromatographic peaks result in
higher concentrations at peak apices and, therefore, lower LOQs. To
accommodate the UHPLC time scale, mass spectrometers must perform
the needed experiments on a short time scale. The TripleTOF systems are
designed to keep up with UHPLC peaks.
Key features of TripleTOF systems for
quant/qual workflows
•
58
Resolution and mass accuracy regardless of scan speed – Instruments
employing orbital trapping require slower scanning to get higher
resolution. The TripleTOF system maintains approximately 30K resolution
regardless of analysis speed.
RUO-MKT-01-1583-A
•
•
Resolution and mass accuracy even at low m/z – Traditional time-offlight (TOF) instruments provided good mass accuracy and resolution at
high m/z. The high detector speed of the TripleTOF 5600 system allows
for sufficient resolution at low m/z, providing unambiguous formulae
ssignments when using accurate mass information, as well as the
isotope and fragmentation pattern.
Integrated software packages – To date, no manufacturer has provided
one software package capable of finding metabolites, confirming
sites of metabolism, tracking time course experiments, and doing
inter-species comparisons. MetabolitePilot Software does all these.
DRUG METABOLISM
www.absciex.com
The system was operated in positive electrospray mode using a
DuoSpray™ Ion Source. The information-dependent acquisition
(IDA) method consisted of a TOF MS survey scan (m/z 100–1000)
followed by three TOF MS/MS dependent scans (m/z 50–1000).
The TOF MS scan data was used for quantification, while the
MS/MS data was used for the accurate mass structure elucidation
of detected metabolites.
Mass defect filtering (MDF) has been shown to be a powerful
tool in detecting metabolites that are similar in elemental
composition to the parent. Traditionally, MDF is a two-step
process, where full-scan data is acquired first, then MDF analysis
is performed post-processing to identify peaks of interest. A
second injection is then performed to acquire the MS/MS spectra
of these potential metabolites.
Parent metabolite quantification
The high-speed TOF MS survey scan was used for the dual
purpose of parent compound quantification and detection of
potential metabolites. Using real-time MDF to increase IDA
efficiency, accurate mass product ion spectra were triggered
on potential metabolites and used for structural elucidation.
Quantitative PK data for buspirone parent compounds were easily
processed in MultiQuant™ Software (Figure 3).
Introduction
Combining quantitative bioanalysis and in vivo metabolite
identification (quant/qual) in the same workflow has the potential
to significantly increase the productivity of pharmaceutical drug
discovery and development. Traditionally, quantitative bioanalysis
is performed on samples obtained from pharmacokinetic (PK)
studies, while metabolite identification is done in a separate
analysis often on two different instruments. Due to sensitivity
limitations of traditional LC/MS/MS instruments, studies for
metabolite identification are often performed at much higher,
non-physiologically relevant doses than for PK studies.
Mass spectrometry
Using real-time mass defect filtering to trigger IDA, the TripleTOF
5600 system can acquire both MS and MS/MS data in the same
run, eliminating the need for a second injection. Multiple mass
defect ranges are often required to detect different metabolite
classes. The system is capable of applying multiple filters at the
same time (Figure 2). The software automatically calculates the
mass defects based on the elemental compositions provided and
performs intelligent, priority-based MS/MS triggering. This gives
priority to peaks matching the mass defect range for MS/MS data
acquisition so that both expected and unexpected metabolites
can be detected.
Key benefits of the TripleTOF 5600 system for
quant/qual workflows
•
Sample analysis was performed on the TripleTOF 5600 system
coupled to an Acquity UPLC system. An acetonitrile/water /0.1%
formic acid gradient was used on a Waters BEH C18, 2.1×100
mm, 1.7 μm column. The flow rate was 0.6 mL/min,
and the column was heated to 50°C.
Real-time multiple mass defect filter
Sample preparation
•
•
Chromatography
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Figure 1: Automated pharmacokinetic (PK) profiling and metabolite identification
using the AB SCIEX TripleTOF® 5600 System with MetabolitePilot™ Software and
MultiQuant™ Software packages.
www.absciex.com
DRUG METABOLISM
59
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Figure 2: Automated real-time multiple mass defect filter for MS/MS triggering on
Phase I and II metabolites and glutathione conjugates in information-dependent
acquisition (IDA) mode.
Data from multiple buspirone PK time points were processed in
batch mode in MetabolitePilot Software (Figure 4). In addition to
conventional sample to control comparison, additional detection
algorithms such as MDF were applied at the same time. Mass
defect filters for multiple phase I and II metabolite as well as
cleavage metabolites were automatically calculated based on
parent compound structure and used for data processing. Other
peak finding strategies can also be used such as isotope pattern
filtering and searching for common accurate mass product ions or
neutral losses.
A confirmation score is automatically calculated for each potential
metabolite detected, based on mass accuracy, isotope pattern,
mass defect, and similarity of the MS/MS spectrum to that of the
parent (Figure 5).
A diverse range of buspirone metabolites was successfully
detected (Table 1). Due to the high mass accuracy, an
unambiguous assignment of elemental composition was achieved.
High-resolution, high-mass-accuracy product ion spectra were
obtained without the need for a second injection due to the high
IDA triggering efficiency of the system.
Figure 4: Automated batch processing of multiple pharmacokinetic (PK) time course
samples for metabolite identification in MetabolitePilot™ Software.
RUO-MKT-01-1583-A
Figure 6: Structural elucidation of a mono-hydroxy buspirone metabolite using
PeakView® Software is shown.
Figure 7: Relative quantification of parent and metabolites of buspirone. Peak area
versus time profiles (0-8hr) for buspirone parent and metabolites in rat plasma,
generated using MultiQuant™ Software.
Structural elucidation
Metabolite identification and confirmation using
MetabolitePilot™ Software
60
Figure 3: Buspirone was quantified in rat plasma from TOF MS data using
MultiQuant™ Software.
High mass accuracy for the fragment ions in the product ion
spectra is a particularly valuable aid when identifying the
site of metabolism, because elemental compositions can be
unambiguously assigned to the product ions. The structural
elucidation workspace quickly performs this task (Figure 6).
Relative quantification of metabolites
•
•
•
Once metabolites are identified, it is often very important to
estimate their concentrations and kinetic profile relative to the
parent. This was easily performed by exporting the accurate
masses of the metabolites to MultiQuant™ Software and plotting
the areas vs. time (Figure 7). Because the TOF MS scan is
completely non-targeted, metabolite information can be queried
without the need to reacquire data. This would also aid in
identifying metabolites that are disproportionate across different
species to address MIST questions.
References
Conclusions
•
High-throughput in vivo quant/qual was successfully performed using
the AB SCIEX TripleTOF 5600 system combined with MetabolitePilot™
Software and MultiQuant™ Software.
The high speed and sensitivity of the system enabled metabolite
detection and identification on standard PK plasma samples.
Efficient data processing software and the successful application of
multiple peak-finding strategies resulted in comprehensive phase I and II
metabolite coverage as well as unexpected cleavage metabolites
for buspirone.
Obtaining high-mass accuracy product ion spectra without
compromising speed is invaluable for structure elucidation of
metabolites and the identification of the site of metabolism for soft
spot optimization.
1
King RC, Gundersdorf R, Fernández-Metzler CL, Rapid Commun Mass Spectrom. 2003;
17(21), 2413-22.
2
Li AC, Alton D, Bryant MS, Shou WZ, Rapid Commun Mass Spectrom. 2005;19(14), 1943-50.
3
Xia YQ, Miller JD, Bakhtiar R, Franklin RB, Liu DQ, Rapid Commun Mass Spectrom. 2003;
17(11), 1137-45.
4
Zhang H, Zhang D, Ray K, J. Mass Spectrom. 2003 Oct;38(10), 1110-2.
5
Zhu M, Ma L, Zhang D, Ray K, Zhao W, Humphreys WG, Skiles G, Sanders M, Zhang H, Drug
Metab Dis-pos. 2006 Oct;34(10), 1722-33.
6
Zhang H, Zhu M, Ray KL, Ma L, Zhang D, Rapid Commun Mass Spectrom. 2008 Jul;
22(13):2082-8.
Acknowledgements
The authors would like to acknowledge Drs. Ragu Ramanathan,
Jonathan Josephs, Nirmala Raghavan, Emily Luk, and Yanou Yang
for providing in vivo buspirone rat PK samples for analysis; the
authors would also like to thank Drs. Gary Impey and Christie
Hunter for their critical review.
Figure 5: MetabolitePilot™ Software results window displays multiple forms of metabolite data: phase I and II metabolites with proposed elemental composition, mass
accuracy, % area, extracted ion chromatograms (XIC) of metabolites, TOF MS and
MS/MS spectra, with scoring information, all in one window for easy visualization.
DRUG METABOLISM
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61
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
Figure 2: Automated real-time multiple mass defect filter for MS/MS triggering on
Phase I and II metabolites and glutathione conjugates in information-dependent
acquisition (IDA) mode.
Data from multiple buspirone PK time points were processed in
batch mode in MetabolitePilot Software (Figure 4). In addition to
conventional sample to control comparison, additional detection
algorithms such as MDF were applied at the same time. Mass
defect filters for multiple phase I and II metabolite as well as
cleavage metabolites were automatically calculated based on
parent compound structure and used for data processing. Other
peak finding strategies can also be used such as isotope pattern
filtering and searching for common accurate mass product ions or
neutral losses.
A confirmation score is automatically calculated for each potential
metabolite detected, based on mass accuracy, isotope pattern,
mass defect, and similarity of the MS/MS spectrum to that of the
parent (Figure 5).
A diverse range of buspirone metabolites was successfully
detected (Table 1). Due to the high mass accuracy, an
unambiguous assignment of elemental composition was achieved.
High-resolution, high-mass-accuracy product ion spectra were
obtained without the need for a second injection due to the high
IDA triggering efficiency of the system.
Figure 4: Automated batch processing of multiple pharmacokinetic (PK) time course
samples for metabolite identification in MetabolitePilot™ Software.
RUO-MKT-01-1583-A
Figure 6: Structural elucidation of a mono-hydroxy buspirone metabolite using
PeakView® Software is shown.
Figure 7: Relative quantification of parent and metabolites of buspirone. Peak area
versus time profiles (0-8hr) for buspirone parent and metabolites in rat plasma,
generated using MultiQuant™ Software.
Structural elucidation
Metabolite identification and confirmation using
MetabolitePilot™ Software
60
Figure 3: Buspirone was quantified in rat plasma from TOF MS data using
MultiQuant™ Software.
High mass accuracy for the fragment ions in the product ion
spectra is a particularly valuable aid when identifying the
site of metabolism, because elemental compositions can be
unambiguously assigned to the product ions. The structural
elucidation workspace quickly performs this task (Figure 6).
Relative quantification of metabolites
•
•
•
Once metabolites are identified, it is often very important to
estimate their concentrations and kinetic profile relative to the
parent. This was easily performed by exporting the accurate
masses of the metabolites to MultiQuant™ Software and plotting
the areas vs. time (Figure 7). Because the TOF MS scan is
completely non-targeted, metabolite information can be queried
without the need to reacquire data. This would also aid in
identifying metabolites that are disproportionate across different
species to address MIST questions.
References
Conclusions
•
High-throughput in vivo quant/qual was successfully performed using
the AB SCIEX TripleTOF 5600 system combined with MetabolitePilot™
Software and MultiQuant™ Software.
The high speed and sensitivity of the system enabled metabolite
detection and identification on standard PK plasma samples.
Efficient data processing software and the successful application of
multiple peak-finding strategies resulted in comprehensive phase I and II
metabolite coverage as well as unexpected cleavage metabolites
for buspirone.
Obtaining high-mass accuracy product ion spectra without
compromising speed is invaluable for structure elucidation of
metabolites and the identification of the site of metabolism for soft
spot optimization.
1
King RC, Gundersdorf R, Fernández-Metzler CL, Rapid Commun Mass Spectrom. 2003;
17(21), 2413-22.
2
Li AC, Alton D, Bryant MS, Shou WZ, Rapid Commun Mass Spectrom. 2005;19(14), 1943-50.
3
Xia YQ, Miller JD, Bakhtiar R, Franklin RB, Liu DQ, Rapid Commun Mass Spectrom. 2003;
17(11), 1137-45.
4
Zhang H, Zhang D, Ray K, J. Mass Spectrom. 2003 Oct;38(10), 1110-2.
5
Zhu M, Ma L, Zhang D, Ray K, Zhao W, Humphreys WG, Skiles G, Sanders M, Zhang H, Drug
Metab Dis-pos. 2006 Oct;34(10), 1722-33.
6
Zhang H, Zhu M, Ray KL, Ma L, Zhang D, Rapid Commun Mass Spectrom. 2008 Jul;
22(13):2082-8.
Acknowledgements
The authors would like to acknowledge Drs. Ragu Ramanathan,
Jonathan Josephs, Nirmala Raghavan, Emily Luk, and Yanou Yang
for providing in vivo buspirone rat PK samples for analysis; the
authors would also like to thank Drs. Gary Impey and Christie
Hunter for their critical review.
Figure 5: MetabolitePilot™ Software results window displays multiple forms of metabolite data: phase I and II metabolites with proposed elemental composition, mass
accuracy, % area, extracted ion chromatograms (XIC) of metabolites, TOF MS and
MS/MS spectra, with scoring information, all in one window for easy visualization.
DRUG METABOLISM
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www.absciex.com
DRUG METABOLISM
61
Metabolite Identification with the
QTRAP® 5500 LC/MS/MS System: Sensitivity,
Selectivity, Speed, and Unique Workflows
Key scientific challenges of metabolite ID
•
•
Matrix interferences – Extracting or finding metabolites in the
matrices from in vitro and especially in vivo metabolic studies often
requires both sensitive and specific scan types.
Throughput backlogs – High-throughput analyses, driven by
efficient data collection and reporting, is or rapid detection and
identification of metabolites in drug discovery laboratories.
Key benefits of the QTRAP 5500® System and for
metabolite ID
•
•
•
Sensitivity – The high sensitivity of the QTRAP 5500 system allows
for metabolic profiling at physiologically relevant dosing.
Specificity – The unique scanning functions of the QTRAP 5500
systems combine high specificity and sensitivity for powerful
metabolite ID workflows.
Speed – High scan rates allow the instrument to keep up with
UHPLC chromatography.
Key features of the QTRAP 5500 system and for
metabolite ID
•
•
•
TripleTrap™ Scanning – these scanning functions unique to the
QTRAP systems combine highly specific triple quadrupole scans
with very fast and sensitive linear ion trap scans for powerful
metabolite ID workflows.
Sensitivity – The high sensitivity of the QTRAP 5500 system enables
more metabolites to be found than with previous systems.
Fast scanning – The highest scanning rates and rapid polarity
switching permit the fast data collection necessary to keep pace
with UHPLC chromatography.
Overview
The QTRAP 5500 system reduces the challenges of “identifying
metabolites during drug discovery and development. The
enhanced speed and sensitivity of the QTRAP 5500 system
provides a more efficient workflows for metabolite identification;
the predictive multiple reaction monitoring (pMRM) approach
and the use of multiple precursor ion and/or neutral loss survey
scans in a single analysis, including polarity switching, means the
ability to find and identify lower-level metabolites is better than
ever before. When used with LightSight® Software, the QTRAP
5500 system serves as a workhorse for metabolite detection and
complements accurate mass approaches.
QTRAP 5500 system overview
The QTRAP 5500 system is a completely redesigned platform.
The ion path (Figure 1) consists of a curved collision cell that
permits a reduced platform footprint; additional enhancements to
instrumentation have lead to multiple performance improvements.
The new QJet 2 ion guide is 2.5-times longer (125mm) than
the ion guide first implemented in the API 5000™ System. The
addition of the QJet ion guide offers improved protection from
contamination of the ion optics, but its main function is to
efficiently capture and focus ions into the Q0 region. A major
advancement in the ion path is the new Linear Accelerator™ Trap
Collision Cell, which brings LINAC® Collision Cell technology to
the Q3 quadrupole for improved resolution and simplified tuning
procedure. Additionally, LINAC technology provides improved ion
extraction efficiencies for as much as 100-fold gain in sensitivity
for ion trap scan modes. Additionally, there’s been a reduction
in ion cooling fragmentation times producing superior MS3
results. Together with new electronics, all of the above changes
have resulted in significant performance enhancements in speed,
selectivity and sensitivity.
Improvements include:
Introduction
Metabolite identification is central to many of the activities in
the discovery and development pipeline. From rapid structural
assignment during the discovery process to provide an early
perspective on a drug candidate’s metabolically labile sites
or soft spot analysis, to a more complete characterization of
pharmacokinetic properties to establish dose and toxicity levels,
there’s an ever-increasing demand on throughput. The ability
to find, identify, and confirm metabolites as fast as possible is
critical, for both major and very low-level metabolites. Doing this
in a single analysis is a highly sought after goal. The utility of
QTRAP system technology with its unique combination of triple
quadrupole and linear ion trap (LIT) scan capabilities together with
automated approaches to metabolite identification has brought
this goal closer to reality.
• Increased quadrupole sensitivity (MRM response, and
signal-to-noise, product ion and neutral loss)
• Enhanced sensitivity 10-100-fold sensitivity improvement
in trap modes (EPI MS/MS)
• More rapid (up to 4-fold faster) QqQ scanning
• Faster polarity switching (50 ms)
• Faster (up to 5-fold) LIT scanning (20,000 Da/ s)
The enhanced speed and sensitivity of the QTRAP 5500 system
means a more efficient workflow for metabolite identification;
the (pMRM) approach and the use of multiple precursor ion
and/or neutral loss survey scans in a single analysis, including
polarity switching, means the ability to find and identify
lower-level metabolites is better than ever before. Below,
challenging metabolite ID applications that are simplified
with the QTRAP 5500 system are discussed.
Metabolite ID examples
• In vivo analyses
• Microdosing studies
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
• Metabolic stability studies
• GSH conjugate screening (reactive metabolites)
In vivo analyses
Detecting metabolites from in vivo samples at clinically relevant
concentrations is a critical challenge for drug discovery and
development. A wide range of complex matrices (bile, plasma,
urine, and fecal extracts) can pose a number of critical issues and
challenges during metabolite analysis, including the requirement
for high sensitivity to detect low levels of circulating metabolites.
Furthermore, these biofluids can cause ion suppression,
further decreasing in sensitivity. Additionally, the complexity
of background signals from biological matrices increases the
complexity of MS and MS/MS data analysis. In general, the
selective nature of MRM, precursor, and neutral loss scans
provide the highest level of detection compared to any single
MS-based methodology.
• Shorter fill time capabilities (50 μs) for faster analyses
The QTRAP 5500 system brings a number of key advantages
to metabolite identification. The addition of the QJet® 2 Ion
Guide and improved Q3 LIT increases signal response 9-fold in
quadrupole mode and 10- to 100-fold in LIT mode. The overall
duty cycle is further improved with trap fill times as short at 50 μs
due to linear ion trap sensitivity increases, and polarity switching
as low as 50 ms. The system also has enhanced scan rates of up
to 20,000 Da/s in LIT mode that are 4-fold faster in quadrupole
mode. This greater cycle rate allows for compatibility with UHPLC
time scales as well as improved information-dependent acquisition
(IDA) coverage provided by a greater number of
MS/MS-dependent scans on relevant peaks.
This tech note describes several key strategies to identify
metabolites in a single analysis.
Figure 1: The ion path of the QTRAP® 5500 System.
62
RUO-MKT-01-1583-A
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DRUG METABOLISM
63
Metabolite Identification with the
QTRAP® 5500 LC/MS/MS System: Sensitivity,
Selectivity, Speed, and Unique Workflows
Key scientific challenges of metabolite ID
•
•
Matrix interferences – Extracting or finding metabolites in the
matrices from in vitro and especially in vivo metabolic studies often
requires both sensitive and specific scan types.
Throughput backlogs – High-throughput analyses, driven by
efficient data collection and reporting, is or rapid detection and
identification of metabolites in drug discovery laboratories.
Key benefits of the QTRAP 5500® System and for
metabolite ID
•
•
•
Sensitivity – The high sensitivity of the QTRAP 5500 system allows
for metabolic profiling at physiologically relevant dosing.
Specificity – The unique scanning functions of the QTRAP 5500
systems combine high specificity and sensitivity for powerful
metabolite ID workflows.
Speed – High scan rates allow the instrument to keep up with
UHPLC chromatography.
Key features of the QTRAP 5500 system and for
metabolite ID
•
•
•
TripleTrap™ Scanning – these scanning functions unique to the
QTRAP systems combine highly specific triple quadrupole scans
with very fast and sensitive linear ion trap scans for powerful
metabolite ID workflows.
Sensitivity – The high sensitivity of the QTRAP 5500 system enables
more metabolites to be found than with previous systems.
Fast scanning – The highest scanning rates and rapid polarity
switching permit the fast data collection necessary to keep pace
with UHPLC chromatography.
Overview
The QTRAP 5500 system reduces the challenges of “identifying
metabolites during drug discovery and development. The
enhanced speed and sensitivity of the QTRAP 5500 system
provides a more efficient workflows for metabolite identification;
the predictive multiple reaction monitoring (pMRM) approach
and the use of multiple precursor ion and/or neutral loss survey
scans in a single analysis, including polarity switching, means the
ability to find and identify lower-level metabolites is better than
ever before. When used with LightSight® Software, the QTRAP
5500 system serves as a workhorse for metabolite detection and
complements accurate mass approaches.
QTRAP 5500 system overview
The QTRAP 5500 system is a completely redesigned platform.
The ion path (Figure 1) consists of a curved collision cell that
permits a reduced platform footprint; additional enhancements to
instrumentation have lead to multiple performance improvements.
The new QJet 2 ion guide is 2.5-times longer (125mm) than
the ion guide first implemented in the API 5000™ System. The
addition of the QJet ion guide offers improved protection from
contamination of the ion optics, but its main function is to
efficiently capture and focus ions into the Q0 region. A major
advancement in the ion path is the new Linear Accelerator™ Trap
Collision Cell, which brings LINAC® Collision Cell technology to
the Q3 quadrupole for improved resolution and simplified tuning
procedure. Additionally, LINAC technology provides improved ion
extraction efficiencies for as much as 100-fold gain in sensitivity
for ion trap scan modes. Additionally, there’s been a reduction
in ion cooling fragmentation times producing superior MS3
results. Together with new electronics, all of the above changes
have resulted in significant performance enhancements in speed,
selectivity and sensitivity.
Improvements include:
Introduction
Metabolite identification is central to many of the activities in
the discovery and development pipeline. From rapid structural
assignment during the discovery process to provide an early
perspective on a drug candidate’s metabolically labile sites
or soft spot analysis, to a more complete characterization of
pharmacokinetic properties to establish dose and toxicity levels,
there’s an ever-increasing demand on throughput. The ability
to find, identify, and confirm metabolites as fast as possible is
critical, for both major and very low-level metabolites. Doing this
in a single analysis is a highly sought after goal. The utility of
QTRAP system technology with its unique combination of triple
quadrupole and linear ion trap (LIT) scan capabilities together with
automated approaches to metabolite identification has brought
this goal closer to reality.
• Increased quadrupole sensitivity (MRM response, and
signal-to-noise, product ion and neutral loss)
• Enhanced sensitivity 10-100-fold sensitivity improvement
in trap modes (EPI MS/MS)
• More rapid (up to 4-fold faster) QqQ scanning
• Faster polarity switching (50 ms)
• Faster (up to 5-fold) LIT scanning (20,000 Da/ s)
The enhanced speed and sensitivity of the QTRAP 5500 system
means a more efficient workflow for metabolite identification;
the (pMRM) approach and the use of multiple precursor ion
and/or neutral loss survey scans in a single analysis, including
polarity switching, means the ability to find and identify
lower-level metabolites is better than ever before. Below,
challenging metabolite ID applications that are simplified
with the QTRAP 5500 system are discussed.
Metabolite ID examples
• In vivo analyses
• Microdosing studies
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
• Metabolic stability studies
• GSH conjugate screening (reactive metabolites)
In vivo analyses
Detecting metabolites from in vivo samples at clinically relevant
concentrations is a critical challenge for drug discovery and
development. A wide range of complex matrices (bile, plasma,
urine, and fecal extracts) can pose a number of critical issues and
challenges during metabolite analysis, including the requirement
for high sensitivity to detect low levels of circulating metabolites.
Furthermore, these biofluids can cause ion suppression,
further decreasing in sensitivity. Additionally, the complexity
of background signals from biological matrices increases the
complexity of MS and MS/MS data analysis. In general, the
selective nature of MRM, precursor, and neutral loss scans
provide the highest level of detection compared to any single
MS-based methodology.
• Shorter fill time capabilities (50 μs) for faster analyses
The QTRAP 5500 system brings a number of key advantages
to metabolite identification. The addition of the QJet® 2 Ion
Guide and improved Q3 LIT increases signal response 9-fold in
quadrupole mode and 10- to 100-fold in LIT mode. The overall
duty cycle is further improved with trap fill times as short at 50 μs
due to linear ion trap sensitivity increases, and polarity switching
as low as 50 ms. The system also has enhanced scan rates of up
to 20,000 Da/s in LIT mode that are 4-fold faster in quadrupole
mode. This greater cycle rate allows for compatibility with UHPLC
time scales as well as improved information-dependent acquisition
(IDA) coverage provided by a greater number of
MS/MS-dependent scans on relevant peaks.
This tech note describes several key strategies to identify
metabolites in a single analysis.
Figure 1: The ion path of the QTRAP® 5500 System.
62
RUO-MKT-01-1583-A
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DRUG METABOLISM
63
In this work, a set of verapamil in vitro hepatocyte metabolites
were spiked into bile matrix at a low level (2.5 μM) to mimic the
in vivo sample matrix. LightSight® Software 2.1 has allowed for
a new novel survey mode called pMRM (predictive MRM). This
new type of IDA experiment can be created automatically using
LightSight Software 2.1 and loaded directly onto the QTRAP
system for rapid analysis. The advantage of pMRM is the improved
signal-to-noise ratio, IDA selectivity, and efficiency compared to
single MS strategies. The QTRAP 5500 system with pMRM survey
mode as well as dual precursor and neutral loss scan mode
was evaluated against a two injection mass defect filter (MDF)
high-resolution experiment “Breakthrough in vivo metabolites
analysis using the QTRAP 5500 system and Predictive MRM”.
Using the targeted pMRM approach, 158 transitions were
created by LightSight Software 2.1 to search for phase I and
II metabolites. A total of 1.5 sec was the total duty cycle for
the method that included automated collection of MS/MS at
20,000 amu/sec. When employing the multiple precursor ion
and neutral loss approach, the overall duty cycle was 2.2 sec.
Both workflows had cycle times well-suited for high-throughput
chromatographic analyses.
To summarize, even at higher-throughput chromatographic
conditions (by a factor of 2), the QTRAP 5500 system found and
identified with confirmatory MS/MS, 7-fold more metabolites
using a single injection workflow than the high-resolution
approach. With the addition of a second injection, the
QTRAP 5500 system still managed to top the high-resolution
experiments, finding 2-fold more metabolites (Figure 2).
Microdosing studies
Many new chemical entities fail in phase I clinical research
studies due to poor pharmacokinetics. Until phase I studies are
completed, human pharmacokinetics must be predicted from
animal models. Microdosing is an approach that may make
human clinical research studies more effective, predictable, and
quicker. The microdosing strategy allows for early assessment
of human pharmacokinetics and the bioavailability of a drug
candidate. Microdosing studies are designed to evaluate
pharmacokinetics or imaging of specific targets without inducing
pharmacologic effects. According to the FDA guidelines, a
microdose is defined as less than 1/100th of a test substance
dose calculated (based on animal data) to yield a pharmacologic
effect, with the maximum dose being ≤100 micrograms.1
Identifying unique and major human metabolites early in the
drug development process allows for the timely assessment of
potential safety issues.
The current technique for evaluating microdosing studies is
accelerator mass spectrometry (AMS). AMS is an ultra-sensitive
technique where the lower limit of quantitation is in the low
fg/mL range. However, this technique has many drawbacks
including the high cost of routine use due to the required
synthesis of radio-labeled compounds. It has been shown2-4
that conventional liquid chromatography/tandem mass
64
RUO-MKT-01-1583-A
QTRAP 5500 system advantages:
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
• Smaller, bench-top-sized footprint
• Made for routine use
• Easy-to-use
• Radio-labeling not required
• High throughput
• Increased QqQ and LIT sensitivity
Metabolic stability studies
Figure 2: Tabulation of all found metabolites, in either single or double injection
workflows. The QTRAP® 5500 System shows far greater detection efficiency than
high-resolution MDF two-injection workflows.
spectrometry (LC/MS/MS) has sufficient sensitivity to support
quantification of parent drug in human microdosing studies.
Comprehensive detection and confirmation of metabolites
can be achieved at microdosing levels in a single injection
using LC/MS/MS. In this work, metabolites from microdoses of
omeprazole were detected and confirmed in rat plasma using a
QTRAP 5500 system. In this study, three jugular vein-cannulated
Sprague-Dawley rats were dosed via oral gavages at 1.67 μg/kg of
omeprazole. Plasma was taken at 20, 40, 60, 80, and 120 min
from each rat. A pMRM method was generated with 129 MRM
transitions with a pause time of 5 msec and dwell time of 5 msec
per transition. MS/MS data was collected automatically via IDA at
a scan rate of 10,000 Da/sec. The overall duty cycle time for this
method was approximately 2.6 sec.
This application is discussed in detail in a technical note titled,
“Detection and Confirmation of Metabolites at Microdosing Levels
Using the New QTRAP 5500 LC/MS/MS System.” Figures 3 and 4
highlight how improved sensitivity of the QTRAP 5500 system has
bolstered for this sensitivity-challenged application area.
To summarize, the sensitivity and speed improvements in the
QTRAP 5500 system have made it the ideal tool for microdosing
studies, in particular for finding and identifying metabolites at
microdosing levels. This system and workflow has significant
advantages over accelerator mass spectrometry (AMS)
technologies. While AMS has been traditionally associated
with microdosing studies because the high sensitivity levels
achieved, there are some drawbacks to the technology, which
are reviewed below:
• Very hardware-intense technology that requires highly qualified
staff to keep it running.
• Preparation of radio-labeled compounds is time consuming and
expensive.
• This technique and it can only evaluate a very limited number of
analytes at once.
• The turnaround time from sample preparation to acquisition to
results is very slow.
The metabolic stability of a drug candidate impacts its
bioavailability and half-life in vivo and should be evaluated
early in the drug discovery and development process. In vitro
experiments using liver microsomes or hepatocytes provide
valuable insights into metabolic stability and aid in the selection of
drug candidates with favorable pharmacokinetic properties. The
identity of the metabolites formed during parent drug metabolism
is also important. Collecting both qualitative (metabolite ID) and
quantitative (metabolic stability, PK, pharmacodynamic) data
at the same time can improve the efficiency within the drug
discovery and development process so that critical data can be
obtained faster.
Figure 3: A minor metabolite (tri-oxidation + glucuronidation) of omeprazole was
detected and confirmed using LightSight® Software.
Figure 5 shows hundreds of predicted metabolites that were
monitored using MRM transitions, and the metabolic stability
of the parent drug was also monitored by following an MRM
transition over a series of time points. The quantitative results
required to determine the half-life while at the same time
monitor the presence of both major and minor metabolites were
monitored at the same time with confirmatory MS/MS all in the
same automated experiment.
GSH-conjugate screening
Screening for reactive metabolites of drug candidates using
GSH trapping is an important part of early safety assessment in
pharmaceutical discovery and development. Due to the toxicity
of reactive metabolites, detection of these species even at trace
levels can be relevant in the optimal design of a therapeutic drug.
Current low- and high-resolution single-MS-based detection
often offers insufficient sensitivity for the detection and
confirmation of GSH-conjugates. The QTRAP 5500 system is the
first LC/MS system that can combine the most powerful modes
of GSH metabolite detection into a single injection workflow
with sufficient sensitivity to ensure accurate results. A series
of groundbreaking workflows with optimized of scan speeds,
quadrupole MS/MS sensitivity, rapid positive/negative switching,
and unprecedented LIT MS/MS confirmation sensitivity will be
presented in this application overview.
Drug-induced idiosyncratic hepatotoxicity is a concern to
pharmaceutical companies, especially because these drugs are
often missed in pre-clinical safety assessments and clinical
research studies, probably due to the low level of reactive
Figure 4: MS/MS of a minor metabolite (tri-oxidation + glucuronidation) of
omeprazole is displayed; m/z 394 is a diagnostic fragment for this metabolite.
metabolite formation.
Reactive metabolites are capable of covalent modification of
proteins or nucleic acids through nucleophilic substitution. These
types of reactions may contribute to idiosyncratic hepatotoxicity
due to the interruption of certain cellular processes. The process
and causes of idiosyncratic drug hepatotoxicity is not fully
understood. However, the formation of reactive metabolites
appears to be associated with various toxicological events.
The presence of GSH-conjugated metabolites is an indication of
the formation of reactive metabolites and is thus critical to identify
and monitor in metabolite profiling studies within the drug
discovery and development process.
None of these issues are apparent with the QTRAP 5500 system.
DRUG METABOLISM
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DRUG METABOLISM
65
In this work, a set of verapamil in vitro hepatocyte metabolites
were spiked into bile matrix at a low level (2.5 μM) to mimic the
in vivo sample matrix. LightSight® Software 2.1 has allowed for
a new novel survey mode called pMRM (predictive MRM). This
new type of IDA experiment can be created automatically using
LightSight Software 2.1 and loaded directly onto the QTRAP
system for rapid analysis. The advantage of pMRM is the improved
signal-to-noise ratio, IDA selectivity, and efficiency compared to
single MS strategies. The QTRAP 5500 system with pMRM survey
mode as well as dual precursor and neutral loss scan mode
was evaluated against a two injection mass defect filter (MDF)
high-resolution experiment “Breakthrough in vivo metabolites
analysis using the QTRAP 5500 system and Predictive MRM”.
Using the targeted pMRM approach, 158 transitions were
created by LightSight Software 2.1 to search for phase I and
II metabolites. A total of 1.5 sec was the total duty cycle for
the method that included automated collection of MS/MS at
20,000 amu/sec. When employing the multiple precursor ion
and neutral loss approach, the overall duty cycle was 2.2 sec.
Both workflows had cycle times well-suited for high-throughput
chromatographic analyses.
To summarize, even at higher-throughput chromatographic
conditions (by a factor of 2), the QTRAP 5500 system found and
identified with confirmatory MS/MS, 7-fold more metabolites
using a single injection workflow than the high-resolution
approach. With the addition of a second injection, the
QTRAP 5500 system still managed to top the high-resolution
experiments, finding 2-fold more metabolites (Figure 2).
Microdosing studies
Many new chemical entities fail in phase I clinical research
studies due to poor pharmacokinetics. Until phase I studies are
completed, human pharmacokinetics must be predicted from
animal models. Microdosing is an approach that may make
human clinical research studies more effective, predictable, and
quicker. The microdosing strategy allows for early assessment
of human pharmacokinetics and the bioavailability of a drug
candidate. Microdosing studies are designed to evaluate
pharmacokinetics or imaging of specific targets without inducing
pharmacologic effects. According to the FDA guidelines, a
microdose is defined as less than 1/100th of a test substance
dose calculated (based on animal data) to yield a pharmacologic
effect, with the maximum dose being ≤100 micrograms.1
Identifying unique and major human metabolites early in the
drug development process allows for the timely assessment of
potential safety issues.
The current technique for evaluating microdosing studies is
accelerator mass spectrometry (AMS). AMS is an ultra-sensitive
technique where the lower limit of quantitation is in the low
fg/mL range. However, this technique has many drawbacks
including the high cost of routine use due to the required
synthesis of radio-labeled compounds. It has been shown2-4
that conventional liquid chromatography/tandem mass
64
RUO-MKT-01-1583-A
QTRAP 5500 system advantages:
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
• Smaller, bench-top-sized footprint
• Made for routine use
• Easy-to-use
• Radio-labeling not required
• High throughput
• Increased QqQ and LIT sensitivity
Metabolic stability studies
Figure 2: Tabulation of all found metabolites, in either single or double injection
workflows. The QTRAP® 5500 System shows far greater detection efficiency than
high-resolution MDF two-injection workflows.
spectrometry (LC/MS/MS) has sufficient sensitivity to support
quantification of parent drug in human microdosing studies.
Comprehensive detection and confirmation of metabolites
can be achieved at microdosing levels in a single injection
using LC/MS/MS. In this work, metabolites from microdoses of
omeprazole were detected and confirmed in rat plasma using a
QTRAP 5500 system. In this study, three jugular vein-cannulated
Sprague-Dawley rats were dosed via oral gavages at 1.67 μg/kg of
omeprazole. Plasma was taken at 20, 40, 60, 80, and 120 min
from each rat. A pMRM method was generated with 129 MRM
transitions with a pause time of 5 msec and dwell time of 5 msec
per transition. MS/MS data was collected automatically via IDA at
a scan rate of 10,000 Da/sec. The overall duty cycle time for this
method was approximately 2.6 sec.
This application is discussed in detail in a technical note titled,
“Detection and Confirmation of Metabolites at Microdosing Levels
Using the New QTRAP 5500 LC/MS/MS System.” Figures 3 and 4
highlight how improved sensitivity of the QTRAP 5500 system has
bolstered for this sensitivity-challenged application area.
To summarize, the sensitivity and speed improvements in the
QTRAP 5500 system have made it the ideal tool for microdosing
studies, in particular for finding and identifying metabolites at
microdosing levels. This system and workflow has significant
advantages over accelerator mass spectrometry (AMS)
technologies. While AMS has been traditionally associated
with microdosing studies because the high sensitivity levels
achieved, there are some drawbacks to the technology, which
are reviewed below:
• Very hardware-intense technology that requires highly qualified
staff to keep it running.
• Preparation of radio-labeled compounds is time consuming and
expensive.
• This technique and it can only evaluate a very limited number of
analytes at once.
• The turnaround time from sample preparation to acquisition to
results is very slow.
The metabolic stability of a drug candidate impacts its
bioavailability and half-life in vivo and should be evaluated
early in the drug discovery and development process. In vitro
experiments using liver microsomes or hepatocytes provide
valuable insights into metabolic stability and aid in the selection of
drug candidates with favorable pharmacokinetic properties. The
identity of the metabolites formed during parent drug metabolism
is also important. Collecting both qualitative (metabolite ID) and
quantitative (metabolic stability, PK, pharmacodynamic) data
at the same time can improve the efficiency within the drug
discovery and development process so that critical data can be
obtained faster.
Figure 3: A minor metabolite (tri-oxidation + glucuronidation) of omeprazole was
detected and confirmed using LightSight® Software.
Figure 5 shows hundreds of predicted metabolites that were
monitored using MRM transitions, and the metabolic stability
of the parent drug was also monitored by following an MRM
transition over a series of time points. The quantitative results
required to determine the half-life while at the same time
monitor the presence of both major and minor metabolites were
monitored at the same time with confirmatory MS/MS all in the
same automated experiment.
GSH-conjugate screening
Screening for reactive metabolites of drug candidates using
GSH trapping is an important part of early safety assessment in
pharmaceutical discovery and development. Due to the toxicity
of reactive metabolites, detection of these species even at trace
levels can be relevant in the optimal design of a therapeutic drug.
Current low- and high-resolution single-MS-based detection
often offers insufficient sensitivity for the detection and
confirmation of GSH-conjugates. The QTRAP 5500 system is the
first LC/MS system that can combine the most powerful modes
of GSH metabolite detection into a single injection workflow
with sufficient sensitivity to ensure accurate results. A series
of groundbreaking workflows with optimized of scan speeds,
quadrupole MS/MS sensitivity, rapid positive/negative switching,
and unprecedented LIT MS/MS confirmation sensitivity will be
presented in this application overview.
Drug-induced idiosyncratic hepatotoxicity is a concern to
pharmaceutical companies, especially because these drugs are
often missed in pre-clinical safety assessments and clinical
research studies, probably due to the low level of reactive
Figure 4: MS/MS of a minor metabolite (tri-oxidation + glucuronidation) of
omeprazole is displayed; m/z 394 is a diagnostic fragment for this metabolite.
metabolite formation.
Reactive metabolites are capable of covalent modification of
proteins or nucleic acids through nucleophilic substitution. These
types of reactions may contribute to idiosyncratic hepatotoxicity
due to the interruption of certain cellular processes. The process
and causes of idiosyncratic drug hepatotoxicity is not fully
understood. However, the formation of reactive metabolites
appears to be associated with various toxicological events.
The presence of GSH-conjugated metabolites is an indication of
the formation of reactive metabolites and is thus critical to identify
and monitor in metabolite profiling studies within the drug
discovery and development process.
None of these issues are apparent with the QTRAP 5500 system.
DRUG METABOLISM
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DRUG METABOLISM
65
Here, data was acquired at the fastest quadrupole scan rate of
2,000 Da/sec for the precursor and neutral loss experiments.
The LIT experiments were acquired at a scan rate of 20,000
Da/sec with a fixed fill time of 35 msec and Q0 trapping to
increase duty cycle. A pos/neg switching time of 50 ms was
used for the two requisite polarity changes needed for this
experiment. All data was processed in LightSight Software 2.1
and the ACD Fragmenter/Specmanager™ to find and assign major
metabolites. Figure 6 shows the XIC trace for precursor ion m/z
272 (-) and neutral loss m/z 129 (+) IDA, and the major GSH
metabolites are shaded in red. A more detailed discussion can
be found in reference 5.
The QTRAP 5500 system showed a significant improvement in
the sensitive detection and confirmation of GSH metabolites
during single injection experiment with high-throughput
chromatographic separation, a proces amenable to early discovery
screening. Furthermore, the improved sensitivity in LIT mode on
the QTRAP 5500 system provides superior fragmentation patterns
and spectrum confirmation over those achieved on less sensitive
accurate mass instruments. The additional dynamic range allows
for a more complete picture of GSH metabolism to be developed
for a drug of interest.
Summary
Highlights
The QTRAP 5500 system provides unique workflow possibilities
as well as enhanced speed, sensitivity, and selectivity. The hybrid
technology combines quadrupole scan functions for finding
potential metabolites with high-sensitivity LIT scans (for identifying
and confirming metabolites. The pMRM approach screens for
hundreds of potential metabolites and triggers the collection of
full-scan MS/MS permitting metabolite identification in a single
analysis. Multiple precursor ion and/or neutral loss combinations,
together with polarity switching, allows for significantly better
workflow strategies for reactive metabolite identification in a
single analysis. With improvements in speed and sensitivity, much
better peak definition is achieved at lower levels, leading to better
identification, even at very high chromatographic throughput.
Next-generation metabolite prediction using LightSight Software
is a perfect complement to instrument capabilities, enabling even
novice metabolite investigators to achieve complete results easily.
•
•
•
•
•
•
•
•
References
1
2
66
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
Food and Drug Administration. Guidance for Industry, investigators, and reviewers –
exploratory IND studies. January 2006.
S.K. Balani, et al., “Evaluation of Microdosing to Assess Pharmacokinetic Linearity in Rats
using Liquid Chromatography-Tandem Mass Spectrometry,” Drug Metab and Dis-pos.
2006; 34: 384-388.
3
M. McLean, et al., “Accelerating Drug Development: Methodology to Support First-in-Man
Pharmacokinetic Studies by the Using of Drug Candidate Microdosing,” Drug Dev Res.
2007; 68: 14-22.
4
J. Ni, et al., “Microdosing Assessment to Evaluate Pharmacokinetics and Drug Metabolism
in Rats using Liquid Chromatography-Tandem Mass Spectrometry,” Pharm Res. 2008.
4
Figure 5: Nefazodone was incubated in rat liver microsomes for 0, 5, 15, and 30 min. The half-life was determined by monitoring the parent drug as well as major and minor
metabolites in the same analysis; metabolites were detected, identified and confirmed together in the same run. A) Parent drug B) metabolic stability of parent drug, C)
oxidative metabolite, D) MSMS of oxidative metabolite.
Improved quadrupole sensitivity (MRM response and signal-to-noise
ratios, parent ion and neutral loss)
Improved sensitivity in trap modes (10-100-fold enhancement in
EPI scans)
Faster QqQ scanning (4-fold)
Faster polarity switching (50 msec)
Faster LIT scanning (20,000 amu/sec)
Shorter fill-time capabilities (50 μsec).
Unique workflows ideal for metabolite detection
• pMRM
• Multiple full-scan survey possibilities (precursor ion, neutral loss,
polarity switching)
Qualitative and quantitative information together
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
“GSH Conjugate Screening with the QTRAP 5500 LC/MS/MS system.”
Figure 6: The extracted ion chromatogram (XIC) trace of +/- neutral loss/parent ion information-dependent acquisition of diclofenac enables the analysis of GSH metabolites
GSH analysis, using a short high pressure gradient.
www.absciex.com
DRUG METABOLISM
67
Here, data was acquired at the fastest quadrupole scan rate of
2,000 Da/sec for the precursor and neutral loss experiments.
The LIT experiments were acquired at a scan rate of 20,000
Da/sec with a fixed fill time of 35 msec and Q0 trapping to
increase duty cycle. A pos/neg switching time of 50 ms was
used for the two requisite polarity changes needed for this
experiment. All data was processed in LightSight Software 2.1
and the ACD Fragmenter/Specmanager™ to find and assign major
metabolites. Figure 6 shows the XIC trace for precursor ion m/z
272 (-) and neutral loss m/z 129 (+) IDA, and the major GSH
metabolites are shaded in red. A more detailed discussion can
be found in reference 5.
The QTRAP 5500 system showed a significant improvement in
the sensitive detection and confirmation of GSH metabolites
during single injection experiment with high-throughput
chromatographic separation, a proces amenable to early discovery
screening. Furthermore, the improved sensitivity in LIT mode on
the QTRAP 5500 system provides superior fragmentation patterns
and spectrum confirmation over those achieved on less sensitive
accurate mass instruments. The additional dynamic range allows
for a more complete picture of GSH metabolism to be developed
for a drug of interest.
Summary
Highlights
The QTRAP 5500 system provides unique workflow possibilities
as well as enhanced speed, sensitivity, and selectivity. The hybrid
technology combines quadrupole scan functions for finding
potential metabolites with high-sensitivity LIT scans (for identifying
and confirming metabolites. The pMRM approach screens for
hundreds of potential metabolites and triggers the collection of
full-scan MS/MS permitting metabolite identification in a single
analysis. Multiple precursor ion and/or neutral loss combinations,
together with polarity switching, allows for significantly better
workflow strategies for reactive metabolite identification in a
single analysis. With improvements in speed and sensitivity, much
better peak definition is achieved at lower levels, leading to better
identification, even at very high chromatographic throughput.
Next-generation metabolite prediction using LightSight Software
is a perfect complement to instrument capabilities, enabling even
novice metabolite investigators to achieve complete results easily.
•
•
•
•
•
•
•
•
References
1
2
66
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
Food and Drug Administration. Guidance for Industry, investigators, and reviewers –
exploratory IND studies. January 2006.
S.K. Balani, et al., “Evaluation of Microdosing to Assess Pharmacokinetic Linearity in Rats
using Liquid Chromatography-Tandem Mass Spectrometry,” Drug Metab and Dis-pos.
2006; 34: 384-388.
3
M. McLean, et al., “Accelerating Drug Development: Methodology to Support First-in-Man
Pharmacokinetic Studies by the Using of Drug Candidate Microdosing,” Drug Dev Res.
2007; 68: 14-22.
4
J. Ni, et al., “Microdosing Assessment to Evaluate Pharmacokinetics and Drug Metabolism
in Rats using Liquid Chromatography-Tandem Mass Spectrometry,” Pharm Res. 2008.
4
Figure 5: Nefazodone was incubated in rat liver microsomes for 0, 5, 15, and 30 min. The half-life was determined by monitoring the parent drug as well as major and minor
metabolites in the same analysis; metabolites were detected, identified and confirmed together in the same run. A) Parent drug B) metabolic stability of parent drug, C)
oxidative metabolite, D) MSMS of oxidative metabolite.
Improved quadrupole sensitivity (MRM response and signal-to-noise
ratios, parent ion and neutral loss)
Improved sensitivity in trap modes (10-100-fold enhancement in
EPI scans)
Faster QqQ scanning (4-fold)
Faster polarity switching (50 msec)
Faster LIT scanning (20,000 amu/sec)
Shorter fill-time capabilities (50 μsec).
Unique workflows ideal for metabolite detection
• pMRM
• Multiple full-scan survey possibilities (precursor ion, neutral loss,
polarity switching)
Qualitative and quantitative information together
LATE STAGE DISCOVERY
LATE STAGE DISCOVERY
For Research Use Only. Not for use in diagnostic procedures.
“GSH Conjugate Screening with the QTRAP 5500 LC/MS/MS system.”
Figure 6: The extracted ion chromatogram (XIC) trace of +/- neutral loss/parent ion information-dependent acquisition of diclofenac enables the analysis of GSH metabolites
GSH analysis, using a short high pressure gradient.
www.absciex.com
DRUG METABOLISM
67
Simultaneous Metabolite Identification and
Quantitation Using UV Data Integration and
LightSight® Software 2.2
Experimental conditions
Alek N. Dooley, Carmai Seto, Hesham Ghobarah, and Elliott B. Jones
Bromocriptine eluted when the gradient was approximately 60%
solution A, to ensure the separation of all metabolites, whether
of increased or decreased polarity. A sample (5 μL) was injected
onto a Phenonomenex HPLC column (50 × 2 mm). Prior to
mass spectrometric analysis, the LC eluant was directed through
a Shimadzu SDP20AV dual wavelength detector set to 302
nm. Predictive multiple reaction monitoring with informationdependent acquisition (pMRM-IDA) methods were created using
LightSight Software 2.2 and run on a 4000 QTRAP system,
a hybrid triple quadrupole/linear ion trap mass spectrometer.
With prior knowledge of the parent drug and its fragmentation
pathway, theoretical MRM transitions for metabolites can be
determined based on a particular biotransformation set. The
pMRM experiments were built using the phase I comprehensive
biotransformation set provided by the LightSight Software. A total
of 166 MRM transitions were included in the pMRM survey scan
linked to a single enhanced product ion (EPI) dependent scan. The
EPI experiment was acquired at a scan rate of 4,000 Da/sec with
dynamic fill time turned on. Structural elucidation of the MS/MS
fragments was achieved with the assistance of the ACD/Labs MS
Fragmenter software package.
AB SCIEX, Redwood City, California and Concord, Ontario, Canada
Key scientific challenges in quant/qual workflows
•
•
Accurate quantification – The mass spectrometric response for the
same molar concentration can vary greatly by compound. Quantifying
a compound using either MS or UV data can be quite useful to avoid
over- or underestimating the amount of a metabolite.
Accurate assignment of the metabolic site – Most workflows require the
use of multiple software packages to identify, elucidate, and quantify
the metabolites.
Key benefits of the LightSight Software 2.2 for
quant/qual workflows
®
•
•
Ease-of-use – LightSight Software can be used to process qualitative
and quantitative data simultaneously.
Increased throughput – Integrated tools for structural elucidation rapidly
facilitate assigning sites of metabolism.
Key features of the LightSight Software 2.2 for
quant/qual workflows
•
•
One comprehensive software package – Quantitative and
qualitative data are easily and automatically generated using
LightSight Software.
Correlation – The software provides peak areas from both the
analog (UV, diode array, or radioactivity) detector and the mass
spectrometric data.
Overview
Integration of UV and MS data is highly desirable for
biotransformation studies. For example, in vitro metabolic stability
studies can provide qualitative metabolism data and kinetics
for the parent compound. Relative quantitation of metabolites
detected by integrating UV data is a very useful workflow;
However manual processing and correlation of both data streams
is time-consuming. Here, qualitative and quantitative data are
collected by acquiring both mass spectrometric and UV data in
the same experiment, and then both data types are processed and
correlated automatically in LightSight Software 2.2.
Bromocriptine was incubated at 50 μM in rabbit liver microsomes
using an NADPH regenerating system under oxidative conditions
at 37 °C. After 1 hr, the incubation was quenched with an equal
volume of acetonitrile. The supernatant was then diluted 1:5 with
water prior to injection, giving a final bromocriptine concentration
of 5 μM in the control.
Results and discussion
Introduction
Drug metabolism studies often estimate the relative abundances
of metabolites alongside the elucidation of metabolite structures
and the assignment of sites of metabolism. While MS-based
detection is a powerful technique for metabolite identification and
structure elucidation, small changes in metabolite structure and
functional groups can often lead to major differences in ionization
efficiency and fragmentation. Therefore, estimating metabolite
concentrations based on the MS signal alone can be difficult
due to the absence of a reference standards for quantitation.
By comparison, UV absorbance is considered to provide a more
uniform response and allows for estimation of the relative
abundances of metabolites.
HIGH RESOLUTION ACCURATE MASS
DEFINITIVE METABOLITE ID
For Research Use Only. Not for use in diagnostic procedures.
Figure 1 shows the molecular structure of bromocriptine with
sites of fragmentation that occur during MS/MS analysis indicated
along with the nominal mass of the fragment. The m/z 346
fragment ion was chosen as the Q3 mass for the pMRM method.
Compounds with higher λmax, above the typical solvent noise that
exists from 100 to 150 nm, typically yield excellent quality data
that correlates nicely with MRM data. Bromocriptine, with a λmax
of 302 nm, exhibits a maximum UV absorbance that has few
interferences with competing background.
Figure 1: Structure and fragmentation of bromocriptine with the m/z ratio for each
fragment shown at the site of cleavage.
If there is complementary analog data (e.g. PDA, UV, ADC)
available, LightSight Software will correlate the MS data to the
analog data and vice versa. The table potential metabolites
contains detected metabolites and both data types to ensure that
all potential metabolites are found even if a metabolite exhibits
only one type of signal. Using the LightSight Software, a total of
14 metabolites were detected and identified, and, of those 14,
11 were oxidative metabolites of bromocriptine. A summary of
the MS and UV data for these metabolites is given Table 1.
Figure 2 shows the correlation between the MS data and the
UV trace for bromocriptine and its metabolites prepared using
LightSight Software.
The metabolites included single, di-, and tri-oxidations of
bromocriptine. The proposed structures of some metabolites
are given in Figure 3. Figure 4 shows the comparison between
a tri-oxidation metabolite of bromocriptine and the parent
bromocriptine MS/MS spectra. The pMRM transition that
corresponds to the triple-oxidation metabolite shows four clear
chromatographic peaks, the presence of which were further
confirmed by the UV trace (Figure 5).
Integration of MS and analog data processing is highly desirable
because drug metabolism scientists can then perform structure
elucidation based on MS/MS data, estimate concentration based
on UV data, and then generate one integrated report.
This technical note describes the use of LightSight Software 2.2
to study the in vitro metabolism of bromocriptine using a 4000
QTRAP® LC/MS/MS System in combination with integrated UV
detection, data processing, and reporting.
Table 1: Metabolites were identified using LightSight® Software sorted by retention time.
68
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
69
Simultaneous Metabolite Identification and
Quantitation Using UV Data Integration and
LightSight® Software 2.2
Experimental conditions
Alek N. Dooley, Carmai Seto, Hesham Ghobarah, and Elliott B. Jones
Bromocriptine eluted when the gradient was approximately 60%
solution A, to ensure the separation of all metabolites, whether
of increased or decreased polarity. A sample (5 μL) was injected
onto a Phenonomenex HPLC column (50 × 2 mm). Prior to
mass spectrometric analysis, the LC eluant was directed through
a Shimadzu SDP20AV dual wavelength detector set to 302
nm. Predictive multiple reaction monitoring with informationdependent acquisition (pMRM-IDA) methods were created using
LightSight Software 2.2 and run on a 4000 QTRAP system,
a hybrid triple quadrupole/linear ion trap mass spectrometer.
With prior knowledge of the parent drug and its fragmentation
pathway, theoretical MRM transitions for metabolites can be
determined based on a particular biotransformation set. The
pMRM experiments were built using the phase I comprehensive
biotransformation set provided by the LightSight Software. A total
of 166 MRM transitions were included in the pMRM survey scan
linked to a single enhanced product ion (EPI) dependent scan. The
EPI experiment was acquired at a scan rate of 4,000 Da/sec with
dynamic fill time turned on. Structural elucidation of the MS/MS
fragments was achieved with the assistance of the ACD/Labs MS
Fragmenter software package.
AB SCIEX, Redwood City, California and Concord, Ontario, Canada
Key scientific challenges in quant/qual workflows
•
•
Accurate quantification – The mass spectrometric response for the
same molar concentration can vary greatly by compound. Quantifying
a compound using either MS or UV data can be quite useful to avoid
over- or underestimating the amount of a metabolite.
Accurate assignment of the metabolic site – Most workflows require the
use of multiple software packages to identify, elucidate, and quantify
the metabolites.
Key benefits of the LightSight Software 2.2 for
quant/qual workflows
®
•
•
Ease-of-use – LightSight Software can be used to process qualitative
and quantitative data simultaneously.
Increased throughput – Integrated tools for structural elucidation rapidly
facilitate assigning sites of metabolism.
Key features of the LightSight Software 2.2 for
quant/qual workflows
•
•
One comprehensive software package – Quantitative and
qualitative data are easily and automatically generated using
LightSight Software.
Correlation – The software provides peak areas from both the
analog (UV, diode array, or radioactivity) detector and the mass
spectrometric data.
Overview
Integration of UV and MS data is highly desirable for
biotransformation studies. For example, in vitro metabolic stability
studies can provide qualitative metabolism data and kinetics
for the parent compound. Relative quantitation of metabolites
detected by integrating UV data is a very useful workflow;
However manual processing and correlation of both data streams
is time-consuming. Here, qualitative and quantitative data are
collected by acquiring both mass spectrometric and UV data in
the same experiment, and then both data types are processed and
correlated automatically in LightSight Software 2.2.
Bromocriptine was incubated at 50 μM in rabbit liver microsomes
using an NADPH regenerating system under oxidative conditions
at 37 °C. After 1 hr, the incubation was quenched with an equal
volume of acetonitrile. The supernatant was then diluted 1:5 with
water prior to injection, giving a final bromocriptine concentration
of 5 μM in the control.
Results and discussion
Introduction
Drug metabolism studies often estimate the relative abundances
of metabolites alongside the elucidation of metabolite structures
and the assignment of sites of metabolism. While MS-based
detection is a powerful technique for metabolite identification and
structure elucidation, small changes in metabolite structure and
functional groups can often lead to major differences in ionization
efficiency and fragmentation. Therefore, estimating metabolite
concentrations based on the MS signal alone can be difficult
due to the absence of a reference standards for quantitation.
By comparison, UV absorbance is considered to provide a more
uniform response and allows for estimation of the relative
abundances of metabolites.
HIGH RESOLUTION ACCURATE MASS
DEFINITIVE METABOLITE ID
For Research Use Only. Not for use in diagnostic procedures.
Figure 1 shows the molecular structure of bromocriptine with
sites of fragmentation that occur during MS/MS analysis indicated
along with the nominal mass of the fragment. The m/z 346
fragment ion was chosen as the Q3 mass for the pMRM method.
Compounds with higher λmax, above the typical solvent noise that
exists from 100 to 150 nm, typically yield excellent quality data
that correlates nicely with MRM data. Bromocriptine, with a λmax
of 302 nm, exhibits a maximum UV absorbance that has few
interferences with competing background.
Figure 1: Structure and fragmentation of bromocriptine with the m/z ratio for each
fragment shown at the site of cleavage.
If there is complementary analog data (e.g. PDA, UV, ADC)
available, LightSight Software will correlate the MS data to the
analog data and vice versa. The table potential metabolites
contains detected metabolites and both data types to ensure that
all potential metabolites are found even if a metabolite exhibits
only one type of signal. Using the LightSight Software, a total of
14 metabolites were detected and identified, and, of those 14,
11 were oxidative metabolites of bromocriptine. A summary of
the MS and UV data for these metabolites is given Table 1.
Figure 2 shows the correlation between the MS data and the
UV trace for bromocriptine and its metabolites prepared using
LightSight Software.
The metabolites included single, di-, and tri-oxidations of
bromocriptine. The proposed structures of some metabolites
are given in Figure 3. Figure 4 shows the comparison between
a tri-oxidation metabolite of bromocriptine and the parent
bromocriptine MS/MS spectra. The pMRM transition that
corresponds to the triple-oxidation metabolite shows four clear
chromatographic peaks, the presence of which were further
confirmed by the UV trace (Figure 5).
Integration of MS and analog data processing is highly desirable
because drug metabolism scientists can then perform structure
elucidation based on MS/MS data, estimate concentration based
on UV data, and then generate one integrated report.
This technical note describes the use of LightSight Software 2.2
to study the in vitro metabolism of bromocriptine using a 4000
QTRAP® LC/MS/MS System in combination with integrated UV
detection, data processing, and reporting.
Table 1: Metabolites were identified using LightSight® Software sorted by retention time.
68
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
69
HIGH RESOLUTION ACCURATE MASS
DEFINITIVE METABOLITE ID
For Research Use Only. Not for use in diagnostic procedures.
Figure 4: Proposed structures are shown for metabolites of bromocriptine detected in the microsomal incubation.
Conclusions
Figure 2: UV and MS data were correlated in the Processing Workspace of LightSight® Software 2.2.
LightSight Software 2.2 efficiently identifies, confirms,
and quantitates metabolites using both MS and analog data.
LightSight Software provides both structure elucidation based
on the MS/MS data and estimated concentration based on UV
in a single analysis.
Figure 5: The extracted ion chromatogram (XIC) for the di-oxidation metabolites of
bromocriptine (top) and the UV chromatogram (bottom).
Figure 3: Comparison of parent and metabolite MS/MS spectra. The MS/MS spectrum for bromocriptine (bottom panel) and a di-oxidation metabolite reveals that all the
fragments at or below m/z 426.1 are common between both spectra, indicating that the sites of metabolism do not occur on these portions of the parent drug.
70
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
71
HIGH RESOLUTION ACCURATE MASS
DEFINITIVE METABOLITE ID
For Research Use Only. Not for use in diagnostic procedures.
Figure 4: Proposed structures are shown for metabolites of bromocriptine detected in the microsomal incubation.
Conclusions
Figure 2: UV and MS data were correlated in the Processing Workspace of LightSight® Software 2.2.
LightSight Software 2.2 efficiently identifies, confirms,
and quantitates metabolites using both MS and analog data.
LightSight Software provides both structure elucidation based
on the MS/MS data and estimated concentration based on UV
in a single analysis.
Figure 5: The extracted ion chromatogram (XIC) for the di-oxidation metabolites of
bromocriptine (top) and the UV chromatogram (bottom).
Figure 3: Comparison of parent and metabolite MS/MS spectra. The MS/MS spectrum for bromocriptine (bottom panel) and a di-oxidation metabolite reveals that all the
fragments at or below m/z 426.1 are common between both spectra, indicating that the sites of metabolism do not occur on these portions of the parent drug.
70
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
71
Differential Mobility Spectrometry for
Quantitative and Qualitative Applications in
Pharmaceutical Workflows
Using SelexION™ Differential Mobility Separation Technology to detect metabolites with better selectivity in
complex mixtures on the AB SCIEX TripleTOF® 5600+ LC/MS/MS System
James A. Ferguson, PhD.
AB SCIEX, 500 Old Connecticut Path, Framingham, MA 01701
Key scientific challenges of metabolite assays
•
•
•
•
•
Materials and methods
There are many sources of interfering compounds that can
complicate the isolation of metabolites during a pharmacokinetics
study. Some interfering compounds come from mobile phases
and columns, some from the sample matrix and, depending on
the chromatography, some isobars can behave like interferences.
Early ADME studies require high throughput and faster
chromatography, which can, in turn, could lead to coeluting
peaks, especially for very similar compounds such as the positional
isomers that can occur from oxidative metabolites or multiple
demethylations (e.g. verapamil). The goal of this study was to
show separation of these structural isomers that cannot be
separated on any existing mass spectrometer. DMS separations
were demonstrated for three oxidation metabolites
of carbamazepine and two demethylation metabolites of
verapamil (Figure 1).
Sample preparation
The data showing the separation of structural isomers (not
shown), indicate that the majority of the compounds moved
further into “negative COV space” as the separation voltages (SV)
increased (see Figure 2 for a schematic and operational overview
of the DMS cell). Nefazodone moved to positive COV space during
DMS transmission optimization runs. Because of this additional
level of separation provided by DMS, the carbamazepine samples
incubated with rat liver microsomes were assessed for metabolites
with improved signal-to-noise ratios. The incubations were
analyzed while ramping COV; when optimal DMS conditions
were found, the samples were re-analyzed using the best SV/COV
combination. The DMS-based chromatogram was nearly identical
to the extracted ion chromatograms of known metabolites and
showed higher signal for some metabolites. This preliminary work
shows some promise for finding metabolites in complex matrices.
Sensitivity – in vivo studies, due to the biological matrices involved,
can negatively impact sensitivity and selectivity.
Limited quantitation range – Lowered sensitivity combined with
often poor selectivity can compromise the desired lower limits of
quantitation (LLOQ).
Limitations in selectivity – In both in vivo and in vitro studies, matrix
ions can be the most abundant ions in the spectra. Pulling out
relevant metabolites rom the background noise can be a challenge
on some systems.
Key benefits of differential mobility separation (DMS) for
metabolite assays
•
Introduction
Enhanced LLOQs – For cases where background noise limits the LOQ,
DMS provides an additional level of selectivity, orthogonal to the mass
spectrometer and LC system.
Better sensitivity – Selectivity gains from DMS permit less involved
sample preparations and/or faster gradients, allowing for overall
improvements in sensitivity and throughput due to more efficient
extractions and better recovery.
Selectivity improvements overcome sensitivity losses – DMS is often
accompanied by a loss in absolute sensitivity, but the gains in selectivity
improve the potential for real gains in LLOQ.
Key features of SelexION™ Differential Mobility Separation
Technology for metabolite assays
•
•
•
•
72
Separation of diverse species reduces baseline noise – SelexION
technology separates isobaric species (true iso-bars in this tech note),
and charge state interferences to reduce background levels and
achieve better selectivity and LOQs – while retaining compatibility
with UHPLC speeds.
Simple installation and maintenance – DMS is truly orthogonal
to LC and MSMS; installs in minutes with no tools required and
no need to break vacuum. Device maintenance is minimal and very
straightforward.
Shortened assay times – SelexION technology can potentially reduce
chromatographic run times.
Efficient separation process – Planar geometry results in short residence
times, high speeds, and minimal diffusion losses for maximum sensitivity
and UHPLC compatibility.
RUO-MKT-01-1583-A
Synthetic oxidative metabolites of carbamazepine (CBZ) and
two verapamil demethylations (Figure 1) from Toronto Research
Chemicals were used to illustrate DMS separation of isobaric
metabolites (structural isomers). To test whether DMS could
minimize background, nefazodone was incubated at 10 µM with
rat liver microsomes. The samples were then quenched with ACN,
lyophilized, and reconstituted in 5% ACN/H2O for analysis.
Chromatography
Pumps and
autosampler: Prominence series from Shimadzu
Column:
Waters Acquity BEH C18 (2.1 × 100 mm),
1.7 µm
Column temp.:
40 °C
Injection:
5 μL
Flow rate:
Programmed (see gradients below)
Mobile phase:
A) 0.1% formic acid
B) Acetonitrile with 0.1% formic acid
HIGH RESOLUTION ACCURATE MASS
DEFINITIVE METABOLITE ID
For Research Use Only. Not for use in diagnostic procedures.
Gradient for the quantitative study:
Time
(min)
%A
%B
Flow
(mL/min)
0.00
97.5
2.5
0.50
0.25
97.5
2.5
—
1.00
20.0
80.0
0.65
1.10
2.5
97.5
—
1.35
2.5
97.5
—
1.40
97.5
2.5
0.65
1.60
97.5
2.5
0.50
2.00
97.5
2.5
END
Gradient for the qualitative (metabolite ID) study:
Time
(min)
%A
%B
•
•
Furthering selectivity with chemical modifiers – Introducing chemical
modifiers to the SelexION device cell allows for amplification of the
separation capacity and adds a new dimension of selectivity.
Ruggedness – SelexION technology provides ruggedness and stability to
enable high performance quantitative bioanalysis.
DRUG METABOLISM
www.absciex.com
Figure 1: Structural Isomers used in this study are shown here.
A = 2-hydroxycarbamazepine, B = 3-hydroxycarbamazepine, C = 10,11
carbamazepine epoxide; all metabolites have the molecular formula C15H12N2O2. D =
norverapamil, and E = p-O-desmethylverapamil; both metabolites have the formula
C26H36N2O4.
www.absciex.com
Flow
(mL/min)
0.00
97.5
2.5
0.50
0.50
97.5
2.5
—
9.50
20.0
80.0
0.65
10.00
2.5
97.5
—
10.50
2.5
97.5
—
11.00
97.5
2.5
0.65
11.25
97.5
2.5
0.50
12.00
97.5
2.5
END
DRUG METABOLISM
73
Differential Mobility Spectrometry for
Quantitative and Qualitative Applications in
Pharmaceutical Workflows
Using SelexION™ Differential Mobility Separation Technology to detect metabolites with better selectivity in
complex mixtures on the AB SCIEX TripleTOF® 5600+ LC/MS/MS System
James A. Ferguson, PhD.
AB SCIEX, 500 Old Connecticut Path, Framingham, MA 01701
Key scientific challenges of metabolite assays
•
•
•
•
•
Materials and methods
There are many sources of interfering compounds that can
complicate the isolation of metabolites during a pharmacokinetics
study. Some interfering compounds come from mobile phases
and columns, some from the sample matrix and, depending on
the chromatography, some isobars can behave like interferences.
Early ADME studies require high throughput and faster
chromatography, which can, in turn, could lead to coeluting
peaks, especially for very similar compounds such as the positional
isomers that can occur from oxidative metabolites or multiple
demethylations (e.g. verapamil). The goal of this study was to
show separation of these structural isomers that cannot be
separated on any existing mass spectrometer. DMS separations
were demonstrated for three oxidation metabolites
of carbamazepine and two demethylation metabolites of
verapamil (Figure 1).
Sample preparation
The data showing the separation of structural isomers (not
shown), indicate that the majority of the compounds moved
further into “negative COV space” as the separation voltages (SV)
increased (see Figure 2 for a schematic and operational overview
of the DMS cell). Nefazodone moved to positive COV space during
DMS transmission optimization runs. Because of this additional
level of separation provided by DMS, the carbamazepine samples
incubated with rat liver microsomes were assessed for metabolites
with improved signal-to-noise ratios. The incubations were
analyzed while ramping COV; when optimal DMS conditions
were found, the samples were re-analyzed using the best SV/COV
combination. The DMS-based chromatogram was nearly identical
to the extracted ion chromatograms of known metabolites and
showed higher signal for some metabolites. This preliminary work
shows some promise for finding metabolites in complex matrices.
Sensitivity – in vivo studies, due to the biological matrices involved,
can negatively impact sensitivity and selectivity.
Limited quantitation range – Lowered sensitivity combined with
often poor selectivity can compromise the desired lower limits of
quantitation (LLOQ).
Limitations in selectivity – In both in vivo and in vitro studies, matrix
ions can be the most abundant ions in the spectra. Pulling out
relevant metabolites rom the background noise can be a challenge
on some systems.
Key benefits of differential mobility separation (DMS) for
metabolite assays
•
Introduction
Enhanced LLOQs – For cases where background noise limits the LOQ,
DMS provides an additional level of selectivity, orthogonal to the mass
spectrometer and LC system.
Better sensitivity – Selectivity gains from DMS permit less involved
sample preparations and/or faster gradients, allowing for overall
improvements in sensitivity and throughput due to more efficient
extractions and better recovery.
Selectivity improvements overcome sensitivity losses – DMS is often
accompanied by a loss in absolute sensitivity, but the gains in selectivity
improve the potential for real gains in LLOQ.
Key features of SelexION™ Differential Mobility Separation
Technology for metabolite assays
•
•
•
•
72
Separation of diverse species reduces baseline noise – SelexION
technology separates isobaric species (true iso-bars in this tech note),
and charge state interferences to reduce background levels and
achieve better selectivity and LOQs – while retaining compatibility
with UHPLC speeds.
Simple installation and maintenance – DMS is truly orthogonal
to LC and MSMS; installs in minutes with no tools required and
no need to break vacuum. Device maintenance is minimal and very
straightforward.
Shortened assay times – SelexION technology can potentially reduce
chromatographic run times.
Efficient separation process – Planar geometry results in short residence
times, high speeds, and minimal diffusion losses for maximum sensitivity
and UHPLC compatibility.
RUO-MKT-01-1583-A
Synthetic oxidative metabolites of carbamazepine (CBZ) and
two verapamil demethylations (Figure 1) from Toronto Research
Chemicals were used to illustrate DMS separation of isobaric
metabolites (structural isomers). To test whether DMS could
minimize background, nefazodone was incubated at 10 µM with
rat liver microsomes. The samples were then quenched with ACN,
lyophilized, and reconstituted in 5% ACN/H2O for analysis.
Chromatography
Pumps and
autosampler: Prominence series from Shimadzu
Column:
Waters Acquity BEH C18 (2.1 × 100 mm),
1.7 µm
Column temp.:
40 °C
Injection:
5 μL
Flow rate:
Programmed (see gradients below)
Mobile phase:
A) 0.1% formic acid
B) Acetonitrile with 0.1% formic acid
HIGH RESOLUTION ACCURATE MASS
DEFINITIVE METABOLITE ID
For Research Use Only. Not for use in diagnostic procedures.
Gradient for the quantitative study:
Time
(min)
%A
%B
Flow
(mL/min)
0.00
97.5
2.5
0.50
0.25
97.5
2.5
—
1.00
20.0
80.0
0.65
1.10
2.5
97.5
—
1.35
2.5
97.5
—
1.40
97.5
2.5
0.65
1.60
97.5
2.5
0.50
2.00
97.5
2.5
END
Gradient for the qualitative (metabolite ID) study:
Time
(min)
%A
%B
•
•
Furthering selectivity with chemical modifiers – Introducing chemical
modifiers to the SelexION device cell allows for amplification of the
separation capacity and adds a new dimension of selectivity.
Ruggedness – SelexION technology provides ruggedness and stability to
enable high performance quantitative bioanalysis.
DRUG METABOLISM
www.absciex.com
Figure 1: Structural Isomers used in this study are shown here.
A = 2-hydroxycarbamazepine, B = 3-hydroxycarbamazepine, C = 10,11
carbamazepine epoxide; all metabolites have the molecular formula C15H12N2O2. D =
norverapamil, and E = p-O-desmethylverapamil; both metabolites have the formula
C26H36N2O4.
www.absciex.com
Flow
(mL/min)
0.00
97.5
2.5
0.50
0.50
97.5
2.5
—
9.50
20.0
80.0
0.65
10.00
2.5
97.5
—
10.50
2.5
97.5
—
11.00
97.5
2.5
0.65
11.25
97.5
2.5
0.50
12.00
97.5
2.5
END
DRUG METABOLISM
73
Using DMS to minimize background for qualitative studies
Figure 2: SelexION™ Technology schematic. SelexION technology uses a planar
geometry to which the term differential mobility spectrometry or DMS can be
applied. The separation voltage (SV) radially displaces ions towards one or the other
electrode depending upon their high and low field mobility characteristics. The
compensation voltage (COV) restores the trajectory of ions from a given compound,
allowing them to transit through the DMS device and enter the mass spectrometer.
SelexION device settings
Results and discussion
The SelexION technology device installs onto the AB SCIEX
TripleTOF® 5600+ LC/MS/MS System in a few minutes without
venting the system and without tools. For the best performance,
the system can be equilibrated for 20–30 min at the desired
temperature. This work was done on a single, experimental
instrument model.
Quantitative analysis
Within the device, ions are separated by differential mobility
due to an individual molecule’s characteristic size and shape.
An optimized combination of separation voltage (SV) and
compensation voltage (COV) is used to separate the selected
analyte from interferences.
These parameters can be optimized very simply as part of
instrument tuning. Optimization of the SV and COV voltages is
performed by constant tee-infusion of the analyte into an LC
flow that corresponds roughly to the eluent composition for
each particular analyte while ramping COV. (a parameter that
can be influenced by mobile phase and source conditions) and
then ramping again at another SV. (Figure 3 shows an example of
this.) The optimal combination of separation and compensation
voltages gives the best separation while maintaining maximum
peak intensity. Somtimes SV and COVcannot be optimized
to achieve separation as was the case for the metabolites of
carbamazepine. When this happens, the SelexION device has
one more tool to use – the addition of a chemical modifier to
the gas stream. For these compounds, isopropanol resulted in
SV/COV combinations that provided optimal separation.
Data acquisition and processing
Sample data were acquired with the Analyst® TF Software 1.6 and
processed using PeakView® Software 2.0.
74
Figure 3: Optimizing the SelexION™ Technology Device. The three traces show the
ramping of the COV with a fixed SV of 3000 V. It was possible to minimize but not
completely eliminate contributions from the other isomers at the voltages indicated
by the bars with colors approximating the colors of the traces for each analyte. The
addition of modifier gave better separation.
RUO-MKT-01-1583-A
While working on quantitative data using positional isomers
(data not shown), it was found that the majority of the
compounds moved further into “negative COV space” with
higher SV. However, nefazodone (NEF) moved to positive COV
space. To test this proof-of-concept, samples incubated with
rat liver microsomes were analyzed in two modes: 1) with the
DMS optimized to transmit NEF and 2) in the transparent mode
to simulate normal MS data acquisition. Figure 8 shows the
chromatograms from the transparent and optimized modes.
Figure 9 shows the XICs of the top metabolites found when
operating in transparent mode and the TIC from the optimized
mode showing nearly identical chromatograms. Table 1 lists the
top metabolites of nefazodone found by each technique.
Figure 5: Transmission of verapamil (VERA) metabolites. XICs for norverapamil
and p-O-desmethylverapamil are shown are shown using a method optimized
for p-O-desmethylverapamil. As in Figure 4, the upper trace has the extracted ion
chromatograms (XICs) overlaid, and the lower is the 3-D view. The transmission of
norverapamil is essentially the same as that of the blank under optimized conditions
for the other desmethyl isomer.
In this study, the separating power of the SelexION technology
device was tested using three synthetic structural isomers of
oxidized carbamazepine (CBZ) and two isomers of demethylated
verapamil (VERA). For these molecules, only a system with
resolving power beyond that of other current chromatography
systems could be used in order to resolve the extremely small
mass differences arising from the differences in their free energies
of formation). The SV and COV space was mapped for the three
CBZ oxides and for the two desmethyl VERA compounds. Then
SV/COV combinations were chosen to minimize the transmission
of other isomers. While good chromatography (which potentially
limits throughput) can separate the isomers, the system was
run using a very fast gradient to maintain efficiency, making the
isomers co-elute. For some compounds, it might be possible to
take advantage of the fragmentation patterns of the isomers by
monitoring different high-resolultion MRM (MRMHR) transitions to
distinguish between the isomers, but the CBZ isomers had nearly
identical fragmentation pattern differences, so separating CBZ
isomers using the collision cell was not possible.
Figure 4: Transmission of the three carbamazepin (CBZ) metabolites. The traces
are for the three isomers with molecular formulae of C12H12N2O using a method
optimized for 2-OH CBZ. The upper trace shows all the extracted ion chromatograms
(XICs) overlaid and the bottom trace shows them in a pseudo-3D view. There is a
small (~ 5%) transmission of 3-OH CBZ (red trace) when the system is optimized for
2-OH CBZ.
Individual compounds and mixtures of the isomers of
hydroxycarbamazepine (OH-CBZ) and desmethylverapamil were
run under the optimized DMS conditions for each individual
analyte using a short gradient to simulate co-elution. Figure
4 shows the transmission of the each OH-CBZs when run
under conditions optimal for the 2-OH-CBZ. Figure 5 shows
both verapamil metabolites under conditions optimal for the
transmission of p-O-desmethylverapamil.
HIGH RESOLUTION ACCURATE MASS
DEFINITIVE METABOLITE ID
For Research Use Only. Not for use in diagnostic procedures.
Figure 6: Calibration curve for 2-OH CBZ from 1–100 ng/mL. Samples with a
mixture of all three CBZ oxidations were also run at 1 and 100 ng/mL for each oxide
and shown in the table as quality control (QC) samples. If the other oxides were
essentially removed from the chromatogram by the differential mobility spectrometry
(DMS), the % accuracies should be about the same for the standards and the QCs
(which is, indeed, the case).
Calibration curves for 2-OH CBZ and CBZ epoxide are shown in
Figure 6 and Figure 7.
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
75
Using DMS to minimize background for qualitative studies
Figure 2: SelexION™ Technology schematic. SelexION technology uses a planar
geometry to which the term differential mobility spectrometry or DMS can be
applied. The separation voltage (SV) radially displaces ions towards one or the other
electrode depending upon their high and low field mobility characteristics. The
compensation voltage (COV) restores the trajectory of ions from a given compound,
allowing them to transit through the DMS device and enter the mass spectrometer.
SelexION device settings
Results and discussion
The SelexION technology device installs onto the AB SCIEX
TripleTOF® 5600+ LC/MS/MS System in a few minutes without
venting the system and without tools. For the best performance,
the system can be equilibrated for 20–30 min at the desired
temperature. This work was done on a single, experimental
instrument model.
Quantitative analysis
Within the device, ions are separated by differential mobility
due to an individual molecule’s characteristic size and shape.
An optimized combination of separation voltage (SV) and
compensation voltage (COV) is used to separate the selected
analyte from interferences.
These parameters can be optimized very simply as part of
instrument tuning. Optimization of the SV and COV voltages is
performed by constant tee-infusion of the analyte into an LC
flow that corresponds roughly to the eluent composition for
each particular analyte while ramping COV. (a parameter that
can be influenced by mobile phase and source conditions) and
then ramping again at another SV. (Figure 3 shows an example of
this.) The optimal combination of separation and compensation
voltages gives the best separation while maintaining maximum
peak intensity. Somtimes SV and COVcannot be optimized
to achieve separation as was the case for the metabolites of
carbamazepine. When this happens, the SelexION device has
one more tool to use – the addition of a chemical modifier to
the gas stream. For these compounds, isopropanol resulted in
SV/COV combinations that provided optimal separation.
Data acquisition and processing
Sample data were acquired with the Analyst® TF Software 1.6 and
processed using PeakView® Software 2.0.
74
Figure 3: Optimizing the SelexION™ Technology Device. The three traces show the
ramping of the COV with a fixed SV of 3000 V. It was possible to minimize but not
completely eliminate contributions from the other isomers at the voltages indicated
by the bars with colors approximating the colors of the traces for each analyte. The
addition of modifier gave better separation.
RUO-MKT-01-1583-A
While working on quantitative data using positional isomers
(data not shown), it was found that the majority of the
compounds moved further into “negative COV space” with
higher SV. However, nefazodone (NEF) moved to positive COV
space. To test this proof-of-concept, samples incubated with
rat liver microsomes were analyzed in two modes: 1) with the
DMS optimized to transmit NEF and 2) in the transparent mode
to simulate normal MS data acquisition. Figure 8 shows the
chromatograms from the transparent and optimized modes.
Figure 9 shows the XICs of the top metabolites found when
operating in transparent mode and the TIC from the optimized
mode showing nearly identical chromatograms. Table 1 lists the
top metabolites of nefazodone found by each technique.
Figure 5: Transmission of verapamil (VERA) metabolites. XICs for norverapamil
and p-O-desmethylverapamil are shown are shown using a method optimized
for p-O-desmethylverapamil. As in Figure 4, the upper trace has the extracted ion
chromatograms (XICs) overlaid, and the lower is the 3-D view. The transmission of
norverapamil is essentially the same as that of the blank under optimized conditions
for the other desmethyl isomer.
In this study, the separating power of the SelexION technology
device was tested using three synthetic structural isomers of
oxidized carbamazepine (CBZ) and two isomers of demethylated
verapamil (VERA). For these molecules, only a system with
resolving power beyond that of other current chromatography
systems could be used in order to resolve the extremely small
mass differences arising from the differences in their free energies
of formation). The SV and COV space was mapped for the three
CBZ oxides and for the two desmethyl VERA compounds. Then
SV/COV combinations were chosen to minimize the transmission
of other isomers. While good chromatography (which potentially
limits throughput) can separate the isomers, the system was
run using a very fast gradient to maintain efficiency, making the
isomers co-elute. For some compounds, it might be possible to
take advantage of the fragmentation patterns of the isomers by
monitoring different high-resolultion MRM (MRMHR) transitions to
distinguish between the isomers, but the CBZ isomers had nearly
identical fragmentation pattern differences, so separating CBZ
isomers using the collision cell was not possible.
Figure 4: Transmission of the three carbamazepin (CBZ) metabolites. The traces
are for the three isomers with molecular formulae of C12H12N2O using a method
optimized for 2-OH CBZ. The upper trace shows all the extracted ion chromatograms
(XICs) overlaid and the bottom trace shows them in a pseudo-3D view. There is a
small (~ 5%) transmission of 3-OH CBZ (red trace) when the system is optimized for
2-OH CBZ.
Individual compounds and mixtures of the isomers of
hydroxycarbamazepine (OH-CBZ) and desmethylverapamil were
run under the optimized DMS conditions for each individual
analyte using a short gradient to simulate co-elution. Figure
4 shows the transmission of the each OH-CBZs when run
under conditions optimal for the 2-OH-CBZ. Figure 5 shows
both verapamil metabolites under conditions optimal for the
transmission of p-O-desmethylverapamil.
HIGH RESOLUTION ACCURATE MASS
DEFINITIVE METABOLITE ID
For Research Use Only. Not for use in diagnostic procedures.
Figure 6: Calibration curve for 2-OH CBZ from 1–100 ng/mL. Samples with a
mixture of all three CBZ oxidations were also run at 1 and 100 ng/mL for each oxide
and shown in the table as quality control (QC) samples. If the other oxides were
essentially removed from the chromatogram by the differential mobility spectrometry
(DMS), the % accuracies should be about the same for the standards and the QCs
(which is, indeed, the case).
Calibration curves for 2-OH CBZ and CBZ epoxide are shown in
Figure 6 and Figure 7.
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
75
Conclusions
This preliminary work was completed on a prototype DMS
instrument, and there are certainly more analytes and more
replicates for each analyte that must be investigated. The results
seem to indicate that the SelexION technology device can, at
least for some compounds, be used to differentiate between
structural isomers that even a high-resolution mass spectrometer
cannot resolve.
Table1: Top metabolites of nefazodone found. Metabolites above are approximately 1% of total area in descending order by relative area. The table on the left lists
metabolites found when the samples were analyzed on a system without differential mobility spectrometry (DMS). The table on the right shows metabolites found when the
same sample was anlyzed with DMS. The data for % area for each metabolite in the two tables are quite similar.
Figure 7: Calibration curve for carbamazepine (CBZ) epoxide from 1–100 ng/mL.
Recovery of the QC samples (see figure 6 caption) again shows that DMS gives
enough separation from the other two oxides.
Similarly, the data from the nefazodone in vitro incubations
indicate that the use of DMS should be further investigated for
lowering the background noise when running more complicated
in vivo samples. For instance, more experiments are needed to see
if any metabolites were overlooked when the SelexION technology
device on the system.
It is certain that DMS can provide useful orthogonal selectivity
during the quantification of structural isomers. The true gain
in sensitivity as a function of absolute sensitivity and selectivity
will be analyte-dependant and will also be influenced by
chromatographic conditions and sample preparation techniques.
References
1
Schneider BB, Covey TR, Coy SL, Krylov VE, Nazarov EG, Anal.Chem. 2010; 82: 1867-1880.
Schneider BB, Covey TR, Coy SL, Krylov VE, Nazarov EG, ThOC am 08:50, Proceedings
of 58nd ASMS Conference on Mass Spectrometry and Allied Topics, Salt Lake City,
May 23-27 2010.
2
HIGH RESOLUTION ACCURATE MASS
DEFINITIVE METABOLITE ID
For Research Use Only. Not for use in diagnostic procedures.
Figure 9: Overlaid extracted ion chromatograms (XIC) and total ion chromatograms
(TIC). The two traces above are the XICs of the major metabolites (intensities
above ~1% of total area) with DMS in transparent mode (blue trace), and the TIC
(unextracted) DMS optimized for nefazodone (NEF) (pink trace) zoomed in to the
area where the metabolites are eluting. The two chromatograms look remarkably
similar. It is interesting to note that in addition to how clean the chromatogram
is with DMS optimized, it is also higher in intensity, which is possibly due to less
interference from co-eluting peaks.
Figure 8: Overlaid TICs. The two traces above are the total ion chromatogram (TIC)
from operating with differential mobility spectrometry (DMS) in transparent mode
(blue trace), and DMS optimized for nefazodone (pink trace). It is clear that the
background is much lower in the pink trace, with DMS optimized for this analyte.
76
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
77
Conclusions
This preliminary work was completed on a prototype DMS
instrument, and there are certainly more analytes and more
replicates for each analyte that must be investigated. The results
seem to indicate that the SelexION technology device can, at
least for some compounds, be used to differentiate between
structural isomers that even a high-resolution mass spectrometer
cannot resolve.
Table1: Top metabolites of nefazodone found. Metabolites above are approximately 1% of total area in descending order by relative area. The table on the left lists
metabolites found when the samples were analyzed on a system without differential mobility spectrometry (DMS). The table on the right shows metabolites found when the
same sample was anlyzed with DMS. The data for % area for each metabolite in the two tables are quite similar.
Figure 7: Calibration curve for carbamazepine (CBZ) epoxide from 1–100 ng/mL.
Recovery of the QC samples (see figure 6 caption) again shows that DMS gives
enough separation from the other two oxides.
Similarly, the data from the nefazodone in vitro incubations
indicate that the use of DMS should be further investigated for
lowering the background noise when running more complicated
in vivo samples. For instance, more experiments are needed to see
if any metabolites were overlooked when the SelexION technology
device on the system.
It is certain that DMS can provide useful orthogonal selectivity
during the quantification of structural isomers. The true gain
in sensitivity as a function of absolute sensitivity and selectivity
will be analyte-dependant and will also be influenced by
chromatographic conditions and sample preparation techniques.
References
1
Schneider BB, Covey TR, Coy SL, Krylov VE, Nazarov EG, Anal.Chem. 2010; 82: 1867-1880.
Schneider BB, Covey TR, Coy SL, Krylov VE, Nazarov EG, ThOC am 08:50, Proceedings
of 58nd ASMS Conference on Mass Spectrometry and Allied Topics, Salt Lake City,
May 23-27 2010.
2
HIGH RESOLUTION ACCURATE MASS
DEFINITIVE METABOLITE ID
For Research Use Only. Not for use in diagnostic procedures.
Figure 9: Overlaid extracted ion chromatograms (XIC) and total ion chromatograms
(TIC). The two traces above are the XICs of the major metabolites (intensities
above ~1% of total area) with DMS in transparent mode (blue trace), and the TIC
(unextracted) DMS optimized for nefazodone (NEF) (pink trace) zoomed in to the
area where the metabolites are eluting. The two chromatograms look remarkably
similar. It is interesting to note that in addition to how clean the chromatogram
is with DMS optimized, it is also higher in intensity, which is possibly due to less
interference from co-eluting peaks.
Figure 8: Overlaid TICs. The two traces above are the total ion chromatogram (TIC)
from operating with differential mobility spectrometry (DMS) in transparent mode
(blue trace), and DMS optimized for nefazodone (pink trace). It is clear that the
background is much lower in the pink trace, with DMS optimized for this analyte.
76
RUO-MKT-01-1583-A
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
77
Solving Bottlenecks in Metabolite
Identification Using TripleTOF® Systems
and MetabolitePilot™ Software
Introduction
High performance solution with innovative capabilities and integrated software to improve efficiency
Key scientific challenges of metabolite ID workflows
•
•
•
Productivity – Increasing productivity in drug discovery and
development continues to be a primary goal in
pharmaceutical research.
Analysis at relevant doses – For many compounds, ADME studies have
been completed at a high, non-therapeutic concentration to monitor
the metabolism process. This can sometimes lead to false conclusions
because some metabolic pathways are concentration-dependent for
some compounds.
High performance software – In the past, the work of finding,
confirming, and quantifying metabolites has required the use of
multiple software packages. MetabolitePilot™ Software can perform
all three functions in one software package.
Key benefits of the TripleTOF® 5600 System and
MetabolitePilot Software for metabolite ID workflows
•
•
•
•
•
•
78
Innovative instrument design – combining the proven high
performance triple quadrupole front end with the Accelerator
TOF™ Analyzer, a state of the art accurate mass analyzer with
unprecedented sensitivity, speed, resolution, and mass accuracy.
Smarter acquisition – The introduction of real-time mass defect
triggering allows the TripleTOF 5600 system to automatically acquire
MS/MS on low-level metabolites. Now, the TripleTOF 5600 system
can trigger on ions with mass defects related to the parent drug at
intensities lower than other compounds, allowing the proverbial needle
to be pulled from its haystack.
Rapid scanning speeds – The TripleTOF 5600 system can obtain up to
100 high-resolution MS/MS spectra per second, which accommodates
fast chromatography while still obtaining comprehensive MS and MS/
MS data and without sacrificing resolution.
High sensitivity – The TripleTOF 5600 system is sensitive enough to
detect metabolites at low substrate concentrations in vitro and from
in vivo PK samples.
A comprehensive metabolite ID package – MetabolitePilot Software
is an integrated, feature-rich, and easy-to-use software package that
combines multiple peak-finding strategies, structural assignment
algorithms, MS/MS fragment interpretation modules, and multi-sample
correlation in a single software package.
Combined quant/qual workflows – The excellent detector dynamic
range for quant in full-scan mode and the high mass accuracy in MS
and MS/MS modes enable comprehensive metabolite identification and
quantification methods.
RUO-MKT-01-1583-A
High-resolution accurate mass LC/MS/MS has become the
technique of choice for metabolite identification. During the
discovery stage, high MS and MS/MS scan speeds and a powerful
full-scan capacity make high-resolution mass spectrometry
well-suited to the detection the of major metabolites for lead
optimization. During the development stage, powerful features
such as mass-defect-triggered information-dependent acquisition
(IDA) and high MS/MS mass accuracy in-depth structure
elucidation and the characterization of in vivo metabolites, even in
complex matrices such as bile.
As high resolution instruments have become increasingly
powerful, manual processing and interpretation of large
amounts of data pose a major challenge. Integrated software to
detect metabolites, perform structure assignment and MS/MS
interpretation, and compare metabolism across multiple samples
is essential to realize the benefits of high-resolution data.
Figure 1: AB SCIEX TripleTOF® 5600 System ion path. Proven front-end technology
coupled to a state-of-the-art Accelerator TOF™ Analyzer has resulted in unparalleled
performance for both quant and qual methods.
The AB SCIEX TripleTOF 4600 and 5600+ systems combined
with MetabolitePilot Software offer an intuitive, fully-integrated
hardware and software solution that sets the standard for speed,
sensitivity, and ease-of-use. This technical note provide
an overview of the key benefits of the TripleTOF system’s
technology for metabolite ID discovery and development.
The TripleTOF systems have revolutionized this workflow by
implementing mass defects as a real-time IDA criterion for
triggering MS/MS in the same run. Multiple mass defect ranges
can be specified in the IDA criteria (Figure 3). Due to its high
acquisition speed, the system is able to perform the mass defect
filtering on each TOF scan immediately after it is acquired.
Advanced technology enables true quant/qual
without compromises
Key features of the TripleTOF 5600 system and
MetabolitePilot Software for metabolite ID workflows
•
•
•
•
Resolution and mass accuracy regardless of scan speed – Instruments
employing orbital trapping require slower scanning to obtain higher
resolution. The TripleTOF systems maintain approximately 30K resolution
regardless of analysis speed. This high-performance, especially in MS/
MS mode, provides unambiguous elemental composition assignment
and identification of the site of metabolism.
MetabolitePilot Software allows batch processing for higher throughput
and more efficient use of the chemist’s time.
Calibration stability and linearity – Traditional time-of-flight (TOF)
instruments typically require internal calibration to maintain mass
accuracy, resulting in lowered signals for analytes and a non-linear
quantitative response. The TripleTOF systems have been designed to
overcome both of these limitations.
Resolution and mass accuracy even at low m/z – Traditional TOF
instruments provided good mass accuracy and resolution at high m/z.
The high detector speed of the TripleTOF systems allow for sufficient
resolution at low m/z, providing unambiguous formulae assigments
using accurate mass, as well as the isotope and fragmention pattern.
DRUG METABOLISM
www.absciex.com
DEFINITIVE METABOLITE ID
DEFINITIVE METABOLITE ID
For Research Use Only. Not for use in diagnostic procedures.
The AB SCIEX TripleTOF systems combine the best attributes of triple
quadrupoles and accurate mass analyzers in a single instrument
(Figure 1). This results in the linearity and sensitivity of a high
performance triple quadrupole for quantitation integrated with the
speed, high mass accuracy (1-2 ppm or less), and high resolution
(30K) of an accurate mass instrument for qualitative analysis. As
a result, it is possible to acquire information-rich data with highresolution TOF MS and MS/MS data in IDA mode on a UHPLC time
scale (Figure 2). This is achieved without sacrificing resolution for
speed, or sensitivity, and mass accuracy for linearity.
The end result is that both high-resolution MS and MS/MS data
is acquired in a single injection, even for complex in vivo samples
and for low-level metabolites (Table 1). This single-step method
not only improves throughput but also makes data analysis and
review faster and simpler, with all the data available to the analyst
for data interpretation.
Real-time multiple mass defect-triggered IDA (mMDF-IDA)
Mass defect filtering is a powerful accurate mass technique to
detect phase I and II metabolites that are related by elemental
composition to the parent compound. This filtering mode is
particularly beneficial in complex, in vivo samples. The traditional
approach involves applying a mass defect filter the data after
acquisition to identify potential metabolite peaks. A second
injection is then performed to obtain the MS/MS spectra of
these peaks. The data is combined to allow a proper evaluation
of each peak. This approach requires two injections and two data
processing stages, which can be time-consuming.
www.absciex.com
Figure 2: Broad coverage for phase I metabolites. An overlay of accurate mass
extracted ion chromatograms (XIC’s) for imipramine incubation at 5 min is shown.
A wide range of imipramine phase I metabolites were easily detected using
a completely generic time-of-flight mass spectrometry TOF-MS informationdependent acquisition (IDA) method in under 2.5 min.
DRUG METABOLISM
79
Solving Bottlenecks in Metabolite
Identification Using TripleTOF® Systems
and MetabolitePilot™ Software
Introduction
High performance solution with innovative capabilities and integrated software to improve efficiency
Key scientific challenges of metabolite ID workflows
•
•
•
Productivity – Increasing productivity in drug discovery and
development continues to be a primary goal in
pharmaceutical research.
Analysis at relevant doses – For many compounds, ADME studies have
been completed at a high, non-therapeutic concentration to monitor
the metabolism process. This can sometimes lead to false conclusions
because some metabolic pathways are concentration-dependent for
some compounds.
High performance software – In the past, the work of finding,
confirming, and quantifying metabolites has required the use of
multiple software packages. MetabolitePilot™ Software can perform
all three functions in one software package.
Key benefits of the TripleTOF® 5600 System and
MetabolitePilot Software for metabolite ID workflows
•
•
•
•
•
•
78
Innovative instrument design – combining the proven high
performance triple quadrupole front end with the Accelerator
TOF™ Analyzer, a state of the art accurate mass analyzer with
unprecedented sensitivity, speed, resolution, and mass accuracy.
Smarter acquisition – The introduction of real-time mass defect
triggering allows the TripleTOF 5600 system to automatically acquire
MS/MS on low-level metabolites. Now, the TripleTOF 5600 system
can trigger on ions with mass defects related to the parent drug at
intensities lower than other compounds, allowing the proverbial needle
to be pulled from its haystack.
Rapid scanning speeds – The TripleTOF 5600 system can obtain up to
100 high-resolution MS/MS spectra per second, which accommodates
fast chromatography while still obtaining comprehensive MS and MS/
MS data and without sacrificing resolution.
High sensitivity – The TripleTOF 5600 system is sensitive enough to
detect metabolites at low substrate concentrations in vitro and from
in vivo PK samples.
A comprehensive metabolite ID package – MetabolitePilot Software
is an integrated, feature-rich, and easy-to-use software package that
combines multiple peak-finding strategies, structural assignment
algorithms, MS/MS fragment interpretation modules, and multi-sample
correlation in a single software package.
Combined quant/qual workflows – The excellent detector dynamic
range for quant in full-scan mode and the high mass accuracy in MS
and MS/MS modes enable comprehensive metabolite identification and
quantification methods.
RUO-MKT-01-1583-A
High-resolution accurate mass LC/MS/MS has become the
technique of choice for metabolite identification. During the
discovery stage, high MS and MS/MS scan speeds and a powerful
full-scan capacity make high-resolution mass spectrometry
well-suited to the detection the of major metabolites for lead
optimization. During the development stage, powerful features
such as mass-defect-triggered information-dependent acquisition
(IDA) and high MS/MS mass accuracy in-depth structure
elucidation and the characterization of in vivo metabolites, even in
complex matrices such as bile.
As high resolution instruments have become increasingly
powerful, manual processing and interpretation of large
amounts of data pose a major challenge. Integrated software to
detect metabolites, perform structure assignment and MS/MS
interpretation, and compare metabolism across multiple samples
is essential to realize the benefits of high-resolution data.
Figure 1: AB SCIEX TripleTOF® 5600 System ion path. Proven front-end technology
coupled to a state-of-the-art Accelerator TOF™ Analyzer has resulted in unparalleled
performance for both quant and qual methods.
The AB SCIEX TripleTOF 4600 and 5600+ systems combined
with MetabolitePilot Software offer an intuitive, fully-integrated
hardware and software solution that sets the standard for speed,
sensitivity, and ease-of-use. This technical note provide
an overview of the key benefits of the TripleTOF system’s
technology for metabolite ID discovery and development.
The TripleTOF systems have revolutionized this workflow by
implementing mass defects as a real-time IDA criterion for
triggering MS/MS in the same run. Multiple mass defect ranges
can be specified in the IDA criteria (Figure 3). Due to its high
acquisition speed, the system is able to perform the mass defect
filtering on each TOF scan immediately after it is acquired.
Advanced technology enables true quant/qual
without compromises
Key features of the TripleTOF 5600 system and
MetabolitePilot Software for metabolite ID workflows
•
•
•
•
Resolution and mass accuracy regardless of scan speed – Instruments
employing orbital trapping require slower scanning to obtain higher
resolution. The TripleTOF systems maintain approximately 30K resolution
regardless of analysis speed. This high-performance, especially in MS/
MS mode, provides unambiguous elemental composition assignment
and identification of the site of metabolism.
MetabolitePilot Software allows batch processing for higher throughput
and more efficient use of the chemist’s time.
Calibration stability and linearity – Traditional time-of-flight (TOF)
instruments typically require internal calibration to maintain mass
accuracy, resulting in lowered signals for analytes and a non-linear
quantitative response. The TripleTOF systems have been designed to
overcome both of these limitations.
Resolution and mass accuracy even at low m/z – Traditional TOF
instruments provided good mass accuracy and resolution at high m/z.
The high detector speed of the TripleTOF systems allow for sufficient
resolution at low m/z, providing unambiguous formulae assigments
using accurate mass, as well as the isotope and fragmention pattern.
DRUG METABOLISM
www.absciex.com
DEFINITIVE METABOLITE ID
DEFINITIVE METABOLITE ID
For Research Use Only. Not for use in diagnostic procedures.
The AB SCIEX TripleTOF systems combine the best attributes of triple
quadrupoles and accurate mass analyzers in a single instrument
(Figure 1). This results in the linearity and sensitivity of a high
performance triple quadrupole for quantitation integrated with the
speed, high mass accuracy (1-2 ppm or less), and high resolution
(30K) of an accurate mass instrument for qualitative analysis. As
a result, it is possible to acquire information-rich data with highresolution TOF MS and MS/MS data in IDA mode on a UHPLC time
scale (Figure 2). This is achieved without sacrificing resolution for
speed, or sensitivity, and mass accuracy for linearity.
The end result is that both high-resolution MS and MS/MS data
is acquired in a single injection, even for complex in vivo samples
and for low-level metabolites (Table 1). This single-step method
not only improves throughput but also makes data analysis and
review faster and simpler, with all the data available to the analyst
for data interpretation.
Real-time multiple mass defect-triggered IDA (mMDF-IDA)
Mass defect filtering is a powerful accurate mass technique to
detect phase I and II metabolites that are related by elemental
composition to the parent compound. This filtering mode is
particularly beneficial in complex, in vivo samples. The traditional
approach involves applying a mass defect filter the data after
acquisition to identify potential metabolite peaks. A second
injection is then performed to obtain the MS/MS spectra of
these peaks. The data is combined to allow a proper evaluation
of each peak. This approach requires two injections and two data
processing stages, which can be time-consuming.
www.absciex.com
Figure 2: Broad coverage for phase I metabolites. An overlay of accurate mass
extracted ion chromatograms (XIC’s) for imipramine incubation at 5 min is shown.
A wide range of imipramine phase I metabolites were easily detected using
a completely generic time-of-flight mass spectrometry TOF-MS informationdependent acquisition (IDA) method in under 2.5 min.
DRUG METABOLISM
79
Information-Dependent
Acquisition (IDA) Method
Metabolites detected by
processing full-scan data
Peak
Intensity
alone
Dynamic
Background
Subtraction
(DBS)
mMDF
with
DBS
31
31
31
Automatically acquired
MS/MS Spectra
17
27
31
Triggering Success Rate
53%
87%
100%
DEFINITIVE METABOLITE ID
DEFINITIVE METABOLITE ID
For Research Use Only. Not for use in diagnostic procedures.
Table 1: Diclofenac metabolites detected in bile using multiple mass defect filtering
(mMDF)-information-dependent acquisition IDA. MS/MS spectra of all the metabolites present were acquired successfully in a single injection analysis.1
Figure 3: Real-time multiple mass defect triggered information-dependent
acquisition (IDA). Mass defect ranges can be selected and programmed in the
Analyst® Software method editor as IDA criteria for MS/MS selection in real time.
Integrated MS/MS fragment interpretation
MS/MS fragment interpretation and assignment to a proposed
metabolite structure is arguably the single most labor-intensive
and time-consuming task when identifying metabolites. Typically
separate software packages were needed for data interpretation
which required switching between two programs. Other software
packages often do not provide integrated storage and reporting
of results, proposed structures, or MS/MS fragment assignments
that support proposed structures.
MetabolitePilot Software has a powerful MS/MS fragment
interpretation function integrated directly in the results
workspace. When reviewing potential metabolites, the scientist
can edit structures and perform automatic fragment assignment
(Figure 4). Both the structure and fragment assignments are saved
in the results table and are directly accessible in data review.
Integrated reports can be generated automatically.
Fragment assignments are automatically highlighted on the
structure when the fragment is selected. The software also
automatically shows possible neutral losses from the selected
fragment to show its relationship to other product ions in the
MS/MS spectrum. A score is assigned to each fragment assignment
to aid in data review. The spectrum can be automatically annotated
with fragment structures and elemental compositions (Figure 4).
Due to the high resolution and mass accuracy of the TripleTOF
systems in MS/MS mode, unambiguous elemental compositions of
product ions can be calculated by the software.
This integrated functionality allows the scientist to quickly
evaluate different structure proposals in an interactive and userfriendly manner. The structure and the entire fragment assignment
file are automatically saved in the results workspace (Figure 5).
Figure 6: Correlation workspace. Integrated, multi-sample correlation enables
comparison of metabolites across a time course study from within MetabolitePilot™
Software. In addition to automatic plots of selected metabolites, chromatographic
and MS/MS data was also overlaid.
Multi-sample correlation
With increased emphasis on MIST guidelines, correlation of
metabolism across multiple samples and estimation of relative
amounts has become a critical part of data interpretation. For
example, the metabolic profile needs to be compared across
multiple species to spot a disproportionate metabolite as early as
possible. In vitro – in vivo correlation and the study of metabolite
kinetics across a time course also require multi-sample correlation.
Traditional software is limited to working with a single sample/
control pair at a time, requiring that the data be exported for
further correlation.
80
RUO-MKT-01-1583-A
Figure 5: Results workspace. Metabolite structures are an integral part of the results
table and are displayed automatically when a metabolite is selected (highlighted in
yellow).
DRUG METABOLISM
www.absciex.com
Conclusions
•
•
Figure 6 illustrates how an entire time course can be easily
evaluated for metabolites formed during an imipramine
microsomal incubation. The data is automatically processed
in batch mode to generate results tables for all time points.
The results for all samples are then selected for correlation.
The software automatically tabulates the abundance of each
metabolite across all samples and overlays all chromatographic,
MS, and MS/MS data as shown for a dioxidation metabolite.
•
Plots of area counts are generated by selecting one or more
metabolites. The user can easily toggle between linear plots and
bar graphs (Figure 7), which is more useful when comparing
metabolism across multiple species. Automatic summary tables
can also be generated showing the area counts or presence or
absence of each metabolite across all samples. Correlation
reports are automatically generated, and all tables and charts
can be easily copied and pasted from the workspace for
preparing presentations.
www.absciex.com
The TripleTOF systems with MetabolitePilot Software significantly
improve bottlenecks that develop during metabolite identification due
to the time-consuming and complex nature of structure elucidation.
Real time multiple mass defect triggered IDA is a powerful method
which enables single injection acquisition of MS/MS data, even in
complex samples and for low-abundance metabolites.
Integrated MS/MS fragment interpretation improves the efficiency
of data analysis by performing fragment assignments on a proposed
structure in a single integrated workspace.
Automatic correlation of UV, MS, and MS/MS data in a single results
workspace provides for faster and easier data review.
Multiple sample correlation goes beyond traditional single sample/
control processing to enable comparison of metabolites across multiple
samples easily and without needing to have manual data handled
manipulation in a separate software program.
•
MetabolitePilot Software can easily correlate metabolites across
multiple samples, automatically generate plots, and overlay
chromatograms, MS, and MS/MS data from multiple samples all
within a single workspace.
Both MS and UV responses can be used as well as other types
of analog data such as beta-RAM. A relative response factor can
be applied for each metabolite individually to correct the MS
response for different metabolites based on the UV signal.
Figure 4: Integrated MS/MS Interpretation. It is simple to toggle between the results
and interpretation workspaces. Within the results workspace, structure editing
and automatic fragment assignment can be performed for a proposed metabolite.
Fragments are assigned and highlighted on the chemical structure for the selected
ion (highlighted in yellow).
Figure 7: Flexible data visualization. The user can quickly switch between line plots
and bar graphs of the selected metabolites for time course studies and inter-species
comparison.
•
References
1
Jie Xing, Kerong Zhang, Minghshe Zhu, et al, WOG PM 2:30, The 59th ASMS Conference on
Mass Spectrometry and Allied Topics, June 5 – 9, 2011, Denver, CO.
Rapid Metabolite Identification using MetabolitePilot™ Software”, AB SCIEX Technical Note,
Publication 2490211- 02.
2“
3
“Simultaneous Pharmacokinetic Profiling and Automated Metabolite Identification using the
AB SCIEX TripleTOF® 5600 System and MetabolitePilot™ Software”, AB SCIEX Technical Note,
Publication 1270210-01.
4
“Solving Bottlenecks in Metabolite ID Data Analysis With MetabolitePilot™ Software”, AB SCIEX
Technical Note, Publication 3610211-01.
5
“Breakthrough Productivity for ADME Studies Using the AB SCIEX TripleTOF® 5600 System”,
AB SCIEX Technical Note, Publication 0480110-01.
6
R. J. Mortishire-Smith et al, “Evaluation of Chemically Intelligent Acquisition and Processing Tools
as a Platform for Discovery Metabolite Identification,” TP 222,The 59th ASMS Conference on
Mass Spectrometry and Allied Topics, June 5 – 9, 2011, Denver, CO.
7
To download a trial version of MetabolitePilot™ Software, please visit: http://www.absciex.com/
Products/Software/MetabolitePilot-Software.
DRUG METABOLISM
81
Information-Dependent
Acquisition (IDA) Method
Metabolites detected by
processing full-scan data
Peak
Intensity
alone
Dynamic
Background
Subtraction
(DBS)
mMDF
with
DBS
31
31
31
Automatically acquired
MS/MS Spectra
17
27
31
Triggering Success Rate
53%
87%
100%
DEFINITIVE METABOLITE ID
DEFINITIVE METABOLITE ID
For Research Use Only. Not for use in diagnostic procedures.
Table 1: Diclofenac metabolites detected in bile using multiple mass defect filtering
(mMDF)-information-dependent acquisition IDA. MS/MS spectra of all the metabolites present were acquired successfully in a single injection analysis.1
Figure 3: Real-time multiple mass defect triggered information-dependent
acquisition (IDA). Mass defect ranges can be selected and programmed in the
Analyst® Software method editor as IDA criteria for MS/MS selection in real time.
Integrated MS/MS fragment interpretation
MS/MS fragment interpretation and assignment to a proposed
metabolite structure is arguably the single most labor-intensive
and time-consuming task when identifying metabolites. Typically
separate software packages were needed for data interpretation
which required switching between two programs. Other software
packages often do not provide integrated storage and reporting
of results, proposed structures, or MS/MS fragment assignments
that support proposed structures.
MetabolitePilot Software has a powerful MS/MS fragment
interpretation function integrated directly in the results
workspace. When reviewing potential metabolites, the scientist
can edit structures and perform automatic fragment assignment
(Figure 4). Both the structure and fragment assignments are saved
in the results table and are directly accessible in data review.
Integrated reports can be generated automatically.
Fragment assignments are automatically highlighted on the
structure when the fragment is selected. The software also
automatically shows possible neutral losses from the selected
fragment to show its relationship to other product ions in the
MS/MS spectrum. A score is assigned to each fragment assignment
to aid in data review. The spectrum can be automatically annotated
with fragment structures and elemental compositions (Figure 4).
Due to the high resolution and mass accuracy of the TripleTOF
systems in MS/MS mode, unambiguous elemental compositions of
product ions can be calculated by the software.
This integrated functionality allows the scientist to quickly
evaluate different structure proposals in an interactive and userfriendly manner. The structure and the entire fragment assignment
file are automatically saved in the results workspace (Figure 5).
Figure 6: Correlation workspace. Integrated, multi-sample correlation enables
comparison of metabolites across a time course study from within MetabolitePilot™
Software. In addition to automatic plots of selected metabolites, chromatographic
and MS/MS data was also overlaid.
Multi-sample correlation
With increased emphasis on MIST guidelines, correlation of
metabolism across multiple samples and estimation of relative
amounts has become a critical part of data interpretation. For
example, the metabolic profile needs to be compared across
multiple species to spot a disproportionate metabolite as early as
possible. In vitro – in vivo correlation and the study of metabolite
kinetics across a time course also require multi-sample correlation.
Traditional software is limited to working with a single sample/
control pair at a time, requiring that the data be exported for
further correlation.
80
RUO-MKT-01-1583-A
Figure 5: Results workspace. Metabolite structures are an integral part of the results
table and are displayed automatically when a metabolite is selected (highlighted in
yellow).
DRUG METABOLISM
www.absciex.com
Conclusions
•
•
Figure 6 illustrates how an entire time course can be easily
evaluated for metabolites formed during an imipramine
microsomal incubation. The data is automatically processed
in batch mode to generate results tables for all time points.
The results for all samples are then selected for correlation.
The software automatically tabulates the abundance of each
metabolite across all samples and overlays all chromatographic,
MS, and MS/MS data as shown for a dioxidation metabolite.
•
Plots of area counts are generated by selecting one or more
metabolites. The user can easily toggle between linear plots and
bar graphs (Figure 7), which is more useful when comparing
metabolism across multiple species. Automatic summary tables
can also be generated showing the area counts or presence or
absence of each metabolite across all samples. Correlation
reports are automatically generated, and all tables and charts
can be easily copied and pasted from the workspace for
preparing presentations.
www.absciex.com
The TripleTOF systems with MetabolitePilot Software significantly
improve bottlenecks that develop during metabolite identification due
to the time-consuming and complex nature of structure elucidation.
Real time multiple mass defect triggered IDA is a powerful method
which enables single injection acquisition of MS/MS data, even in
complex samples and for low-abundance metabolites.
Integrated MS/MS fragment interpretation improves the efficiency
of data analysis by performing fragment assignments on a proposed
structure in a single integrated workspace.
Automatic correlation of UV, MS, and MS/MS data in a single results
workspace provides for faster and easier data review.
Multiple sample correlation goes beyond traditional single sample/
control processing to enable comparison of metabolites across multiple
samples easily and without needing to have manual data handled
manipulation in a separate software program.
•
MetabolitePilot Software can easily correlate metabolites across
multiple samples, automatically generate plots, and overlay
chromatograms, MS, and MS/MS data from multiple samples all
within a single workspace.
Both MS and UV responses can be used as well as other types
of analog data such as beta-RAM. A relative response factor can
be applied for each metabolite individually to correct the MS
response for different metabolites based on the UV signal.
Figure 4: Integrated MS/MS Interpretation. It is simple to toggle between the results
and interpretation workspaces. Within the results workspace, structure editing
and automatic fragment assignment can be performed for a proposed metabolite.
Fragments are assigned and highlighted on the chemical structure for the selected
ion (highlighted in yellow).
Figure 7: Flexible data visualization. The user can quickly switch between line plots
and bar graphs of the selected metabolites for time course studies and inter-species
comparison.
•
References
1
Jie Xing, Kerong Zhang, Minghshe Zhu, et al, WOG PM 2:30, The 59th ASMS Conference on
Mass Spectrometry and Allied Topics, June 5 – 9, 2011, Denver, CO.
Rapid Metabolite Identification using MetabolitePilot™ Software”, AB SCIEX Technical Note,
Publication 2490211- 02.
2“
3
“Simultaneous Pharmacokinetic Profiling and Automated Metabolite Identification using the
AB SCIEX TripleTOF® 5600 System and MetabolitePilot™ Software”, AB SCIEX Technical Note,
Publication 1270210-01.
4
“Solving Bottlenecks in Metabolite ID Data Analysis With MetabolitePilot™ Software”, AB SCIEX
Technical Note, Publication 3610211-01.
5
“Breakthrough Productivity for ADME Studies Using the AB SCIEX TripleTOF® 5600 System”,
AB SCIEX Technical Note, Publication 0480110-01.
6
R. J. Mortishire-Smith et al, “Evaluation of Chemically Intelligent Acquisition and Processing Tools
as a Platform for Discovery Metabolite Identification,” TP 222,The 59th ASMS Conference on
Mass Spectrometry and Allied Topics, June 5 – 9, 2011, Denver, CO.
7
To download a trial version of MetabolitePilot™ Software, please visit: http://www.absciex.com/
Products/Software/MetabolitePilot-Software.
DRUG METABOLISM
81
Removing Bottlenecks in Metabolite ID Data
Analysis with MetabolitePilot™ Software
New multi-sample correlation and integrated MS/MS interpretation improve efficiency of data analysis
Hesham Ghobarah, Alina Dindyal-Popescu, Hai Ying
AB SCIEX, Toronto, Ontario
Key scientific challenges of metabolite ID workflows
•
•
•
Productivity – Increasing productivity in drug discovery and development
continues to be a primary goal in
pharmaceutical research.
Analysis at relevant doses – For many compounds, ADME studies have
been completed at a high, non-therapeutic concentration to monitor
the metabolism process. This can sometimes lead to false conclusions
because some metabolic pathways are concentration-dependent for
some compounds.
•
•
•
•
•
•
82
Innovative instrument design – combining the proven high performance
triple quadrupole front end with the Accelerator TOF™ Analyzer, a state
of the art accurate mass analyzer with unprecedented sensitivity, speed,
resolution, and mass accuracy.
Smarter acquisition – The introduction of real-time mass defect
triggering allows the TripleTOF 5600 system to automatically acquire
MS/MS on low-level metabolites. Now, the TripleTOF 5600 system
can trigger on ions with mass defects related to the parent drug at
intensities lower than other compounds, allowing the proverbial needle
to be pulled from its haystack.
Rapid scanning speeds – The TripleTOF 5600 system can obtain up to
100 high-resolution MS/MS spectra per second, which accommodates
fast chromatography while still obtaining comprehensive MS and MS/
MS data and without sacrificing resolution.
High sensitivity – The TripleTOF 5600 system is sensitive enough to
detect metabolites at low substrate concentrations in vitro and from
in vivo PK samples.
A comprehensive metabolite ID package – MetabolitePilot Software
is an integrated, feature-rich, and easy-to-use software package that
combines multiple peak-finding strategies, structural assignment
algorithms, MS/MS fragment interpretation modules, and multi-sample
correlation in a single software package.
Combined quant/qual workflows – The excellent detector dynamic
range for quant in full-scan mode and the high mass accuracy in MS
and MS/MS modes enable comprehensive metabolite identification and
quantification methods.
RUO-MKT-01-1583-A
correlation. Traditional software is limited to working with
a single sample/control pair at a time. MetabolitePilot
Software can easily perform correlation of metabolites across
multiple samples, automatically generate plots, and overlay
chromatograms, MS, and MS/MS data from multiple samples all
within a single workspace.
Mass Spectrometry – A generic method was used for data
acquisition. The information-dependent acquisition (IDA) method
consisted of a time-of-flight mass spectrometry (TOF MS) survey
scan followed by two TOF MS/MS scans. The mass range was m/z
100–1000 for both MS and MS/MS. The Dynamic Background
Subtraction™ Algorithm was applied for IDA criteria, and a
collision energy of 35 eV with a spread of ±10 eV was used
for the MS/MS scans. External mass calibration was performed
automatically using the calibrant delivery system (CDS). Data
processing was performed using MetabolitePilot Software 1.5.
Figure 1 demonstrates how an entire time course can be easily
evaluated for metabolites arising from a microsome incubation
of imipramine. The data is automatically processed in batch
mode to generate results tables for all time points. The results
for all samples are then selected for correlation. The software
automatically tabulates the abundance of each metabolite across
all samples, and overlays all chromatographic, MS, and MS/MS
data, as shown for a dioxidation metabolite.
Multi-sample correlation
High performance software – In the past, the work of finding,
confirming, and quantifying metabolites has required the use of
multiple software packages. MetabolitePilot™ Software can perform all
three functions in one software package.
Key benefits of the TripleTOF® 600 System and
MetabolitePilot Software for metabolite ID workflows
Chromatography – Sample analysis was performed on
the AB SCIEX TripleTOF® 5600 System coupled with a
Shimadzu Prominence UFLC-XR HPLC system. A generic
acetonitrile/water/0.1% formic acid gradient was used on a
Phenomenex Synergi Polar-RP column 2.5 µm, (2 × 50 mm).
Total run time was 3.7 min. Injection volume was 10 µL.
Key features of the TripleTOF 5600 system and
MetabolitePilot Software for metabolite ID workflows
•
•
•
•
With increased emphasis on MIST guidelines, correlation of
metabolism across multiple samples and estimation of relative
amounts of metabolites has become a critical part of data
interpretation. For example, the metabolic profile needs to be
compared across multiple species to spot a disproportionate
metabolite as early as possible. In vitro – in vivo correlation
and the study of metabolite kinetics also require multisample
DEFINITIVE METABOLITE ID
DEFINITIVE METABOLITE ID
For Research Use Only. Not for use in diagnostic procedures.
Both MS and UV responses can be used as well as other types
of analog data such as beta-RAM. A relative response factor can
be applied for each metabolite individually to correct the MS
response for different metabolites based on the corresponding
UV signal.
Plots of area counts are generated by selecting one or more
metabolites. The user can easily toggle between linear plots and
bar graphs (Figure 2), which is more useful when comparing
metabolism across multiple species. Automatic summary tables
can also be generated showing the area counts or presence/
absence of each metabolite across all samples (Figure 3).
Resolution and mass accuracy regardless of scan speed – Instruments
employing orbital trapping require slower scanning to obtain
higher resolution. The TripleTOF systems maintain approximately
30K resolution regardless of analysis speed. This high-performance,
especially in MS/MS mode, provides unambiguous elemental
composition assignment and identification of the site of metabolism.
MetabolitePilot Software allows batch processing for higher throughput
and more efficient use of the chemist’s time.
Calibration stability and linearity – Traditional time-of-flight (TOF)
instruments typically require internal calibration to maintain mass
accuracy, resulting in lowered signals for analytes and a non-linear
quantitative response. The TripleTOF systems have been designed to
overcome both of these limitations.
Resolution and mass accuracy even at low m/z – Traditional TOF
instruments provided good mass accuracy and resolution at high m/z.
The high detector speed of the TripleTOF systems allow for sufficient
resolution at low m/z, providing unambiguous formulae assigments
using accurate mass, as well as the isotope and fragmention pattern.
Experimental
Sample Preparation – Haloperidol and Imipramine were incubated
in rat liver microsomes at an initial substrate concentration of
1 µM. Standard high-throughput incubation conditions were used
with time points of 0, 5, 15, and 30 min. Protein concentration
was 1 mg/mL, and NADPH was present at 4 mM. The reaction
was quenched using an equal volume of acetonitrile and then
diluted 1:1 with water prior to analysis. The final substrate
concentration at t = 0 was 0.25 µM.
DRUG METABOLISM
www.absciex.com
Figure 1: Correlation workspace. Integrated, multi-sample correlation enables comparison of metabolites across a time course from within MetabolitePilot™ Software.
In addition to automatic plots of selected metabolites, chromatographic and MS/MS data are also overlaid.
www.absciex.com
DRUG METABOLISM
83
Removing Bottlenecks in Metabolite ID Data
Analysis with MetabolitePilot™ Software
New multi-sample correlation and integrated MS/MS interpretation improve efficiency of data analysis
Hesham Ghobarah, Alina Dindyal-Popescu, Hai Ying
AB SCIEX, Toronto, Ontario
Key scientific challenges of metabolite ID workflows
•
•
•
Productivity – Increasing productivity in drug discovery and development
continues to be a primary goal in
pharmaceutical research.
Analysis at relevant doses – For many compounds, ADME studies have
been completed at a high, non-therapeutic concentration to monitor
the metabolism process. This can sometimes lead to false conclusions
because some metabolic pathways are concentration-dependent for
some compounds.
•
•
•
•
•
•
82
Innovative instrument design – combining the proven high performance
triple quadrupole front end with the Accelerator TOF™ Analyzer, a state
of the art accurate mass analyzer with unprecedented sensitivity, speed,
resolution, and mass accuracy.
Smarter acquisition – The introduction of real-time mass defect
triggering allows the TripleTOF 5600 system to automatically acquire
MS/MS on low-level metabolites. Now, the TripleTOF 5600 system
can trigger on ions with mass defects related to the parent drug at
intensities lower than other compounds, allowing the proverbial needle
to be pulled from its haystack.
Rapid scanning speeds – The TripleTOF 5600 system can obtain up to
100 high-resolution MS/MS spectra per second, which accommodates
fast chromatography while still obtaining comprehensive MS and MS/
MS data and without sacrificing resolution.
High sensitivity – The TripleTOF 5600 system is sensitive enough to
detect metabolites at low substrate concentrations in vitro and from
in vivo PK samples.
A comprehensive metabolite ID package – MetabolitePilot Software
is an integrated, feature-rich, and easy-to-use software package that
combines multiple peak-finding strategies, structural assignment
algorithms, MS/MS fragment interpretation modules, and multi-sample
correlation in a single software package.
Combined quant/qual workflows – The excellent detector dynamic
range for quant in full-scan mode and the high mass accuracy in MS
and MS/MS modes enable comprehensive metabolite identification and
quantification methods.
RUO-MKT-01-1583-A
correlation. Traditional software is limited to working with
a single sample/control pair at a time. MetabolitePilot
Software can easily perform correlation of metabolites across
multiple samples, automatically generate plots, and overlay
chromatograms, MS, and MS/MS data from multiple samples all
within a single workspace.
Mass Spectrometry – A generic method was used for data
acquisition. The information-dependent acquisition (IDA) method
consisted of a time-of-flight mass spectrometry (TOF MS) survey
scan followed by two TOF MS/MS scans. The mass range was m/z
100–1000 for both MS and MS/MS. The Dynamic Background
Subtraction™ Algorithm was applied for IDA criteria, and a
collision energy of 35 eV with a spread of ±10 eV was used
for the MS/MS scans. External mass calibration was performed
automatically using the calibrant delivery system (CDS). Data
processing was performed using MetabolitePilot Software 1.5.
Figure 1 demonstrates how an entire time course can be easily
evaluated for metabolites arising from a microsome incubation
of imipramine. The data is automatically processed in batch
mode to generate results tables for all time points. The results
for all samples are then selected for correlation. The software
automatically tabulates the abundance of each metabolite across
all samples, and overlays all chromatographic, MS, and MS/MS
data, as shown for a dioxidation metabolite.
Multi-sample correlation
High performance software – In the past, the work of finding,
confirming, and quantifying metabolites has required the use of
multiple software packages. MetabolitePilot™ Software can perform all
three functions in one software package.
Key benefits of the TripleTOF® 600 System and
MetabolitePilot Software for metabolite ID workflows
Chromatography – Sample analysis was performed on
the AB SCIEX TripleTOF® 5600 System coupled with a
Shimadzu Prominence UFLC-XR HPLC system. A generic
acetonitrile/water/0.1% formic acid gradient was used on a
Phenomenex Synergi Polar-RP column 2.5 µm, (2 × 50 mm).
Total run time was 3.7 min. Injection volume was 10 µL.
Key features of the TripleTOF 5600 system and
MetabolitePilot Software for metabolite ID workflows
•
•
•
•
With increased emphasis on MIST guidelines, correlation of
metabolism across multiple samples and estimation of relative
amounts of metabolites has become a critical part of data
interpretation. For example, the metabolic profile needs to be
compared across multiple species to spot a disproportionate
metabolite as early as possible. In vitro – in vivo correlation
and the study of metabolite kinetics also require multisample
DEFINITIVE METABOLITE ID
DEFINITIVE METABOLITE ID
For Research Use Only. Not for use in diagnostic procedures.
Both MS and UV responses can be used as well as other types
of analog data such as beta-RAM. A relative response factor can
be applied for each metabolite individually to correct the MS
response for different metabolites based on the corresponding
UV signal.
Plots of area counts are generated by selecting one or more
metabolites. The user can easily toggle between linear plots and
bar graphs (Figure 2), which is more useful when comparing
metabolism across multiple species. Automatic summary tables
can also be generated showing the area counts or presence/
absence of each metabolite across all samples (Figure 3).
Resolution and mass accuracy regardless of scan speed – Instruments
employing orbital trapping require slower scanning to obtain
higher resolution. The TripleTOF systems maintain approximately
30K resolution regardless of analysis speed. This high-performance,
especially in MS/MS mode, provides unambiguous elemental
composition assignment and identification of the site of metabolism.
MetabolitePilot Software allows batch processing for higher throughput
and more efficient use of the chemist’s time.
Calibration stability and linearity – Traditional time-of-flight (TOF)
instruments typically require internal calibration to maintain mass
accuracy, resulting in lowered signals for analytes and a non-linear
quantitative response. The TripleTOF systems have been designed to
overcome both of these limitations.
Resolution and mass accuracy even at low m/z – Traditional TOF
instruments provided good mass accuracy and resolution at high m/z.
The high detector speed of the TripleTOF systems allow for sufficient
resolution at low m/z, providing unambiguous formulae assigments
using accurate mass, as well as the isotope and fragmention pattern.
Experimental
Sample Preparation – Haloperidol and Imipramine were incubated
in rat liver microsomes at an initial substrate concentration of
1 µM. Standard high-throughput incubation conditions were used
with time points of 0, 5, 15, and 30 min. Protein concentration
was 1 mg/mL, and NADPH was present at 4 mM. The reaction
was quenched using an equal volume of acetonitrile and then
diluted 1:1 with water prior to analysis. The final substrate
concentration at t = 0 was 0.25 µM.
DRUG METABOLISM
www.absciex.com
Figure 1: Correlation workspace. Integrated, multi-sample correlation enables comparison of metabolites across a time course from within MetabolitePilot™ Software.
In addition to automatic plots of selected metabolites, chromatographic and MS/MS data are also overlaid.
www.absciex.com
DRUG METABOLISM
83
accuracy of the TripleTOF 5600 system in MS/MS mode,
unambiguous elemental compositions of all product ions can be
calculated by the software.
Integrated MS/MS fragment interpretation
This integrated functionality allows the scientist to quickly
evaluate different structure proposals in an interactive and
user-friendly manner. The structure and the entire fragment
assignment are automatically saved in the results workspace as
illustrated in Figure 5.
MS/MS fragment interpretation and assignment to a proposed
metabolite structure is arguably the single most labor-intensive
and time-consuming task when identifying metabolites data
interpretation. Typically separate software packages were needed
for data interpretation which required switching between two
separate programs. Other software programs often do not
provide integrated storage and reporting of results, proposed
structures, and MS/MS fragment assignments that support the
proposed structure.
Figure 2: Flexible data visualization. The user can quickly switch between line
plots and bar graphs of the selected metabolites for time course results and
inter-species comparison.
Figure 3: Summary tables. Concise summary tables of all selected metabolites
across multiple samples are automatically generated in MetabolitePilot™ Software.
The user can display presence or absence (top) and the actual area counts of MS or
UV data (bottom).
84
Correlation reports are automatically generated, and all tables
and charts can be easily copied and pasted from the workspace
for preparing presentations.
RUO-MKT-01-1583-A
Figure 4: Integrated MS/MS interpretation. Directly within the results workspace,
structure editing and automatic fragment assignment can be performed for a
proposed metabolite. Fragments are assigned and highlighted on the
chemical structure.
MetabolitePilot Software has a powerful MS/MS fragment
interpretation function integrated directly in the results workspace
(Figure 4). When reviewing potential metabolites, the scientist can
edit structures and perform automatic fragment assignment. Both
the structure and fragment assignments are saved in the results
table and are directly accessible in data review. Integrated reports
can be generated automatically.
Fragment assignments are automatically highlighted on the
structure when the fragment is selected. The software also
automatically shows possible neutral losses from the selected
fragment to show its relationship to other product ions in
the MS/MS spectrum. A score is assigned to each fragment
assignment to aid in data review. The MS/MS spectrum can be
automatically annotated with fragment structures and elemental
compositions (Figure 4). Due to the high resolution and mass
Conclusions
New advanced functions in MetabolitePilot Software 1.5 help increase
the efficiency of data analysis and interpretation.
Multiple sample correlation goes beyond traditional single
sample/control processing to enable comparison of metabolites
across multiple samples easily and without manual data manipulation
in separate software.
Integrated MS/MS fragment interpretation improves the efficiency
of the most time-consuming aspect of metabolite data analysis by
performing fragment assignments on a proposed structure in a single,
integrated workspace throughput sample preparation and
fast chromatography.
•
•
•
DEFINITIVE METABOLITE ID
DEFINITIVE METABOLITE ID
For Research Use Only. Not for use in diagnostic procedures.
References
Rapid Metabolite Identification Using MetabolitePilot™ Software”, AB SCIEX Technical Note,
Publication 2490211- 01.
1“
2
R. J. Mortishire-Smith et al, “Evaluation of Chemically Intelligent Acquisition and Processing
Tools as a Platform for Discovery Metabolite Identification,” TP 222,The 59th ASMS
Conference on Mass Spectrometry and Allied Topics, June 5 – 9, 2011, Denver, CO.
3
To download a trial version of MetabolitePilot™ software, please visit: http://www.absciex.
com/Products/Software/MetabolitePilot-Software
Figure 5: Results workspace. Metabolite structures are an integral part of the results
table and are displayed automatically when a metabolite is selected.
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
85
accuracy of the TripleTOF 5600 system in MS/MS mode,
unambiguous elemental compositions of all product ions can be
calculated by the software.
Integrated MS/MS fragment interpretation
This integrated functionality allows the scientist to quickly
evaluate different structure proposals in an interactive and
user-friendly manner. The structure and the entire fragment
assignment are automatically saved in the results workspace as
illustrated in Figure 5.
MS/MS fragment interpretation and assignment to a proposed
metabolite structure is arguably the single most labor-intensive
and time-consuming task when identifying metabolites data
interpretation. Typically separate software packages were needed
for data interpretation which required switching between two
separate programs. Other software programs often do not
provide integrated storage and reporting of results, proposed
structures, and MS/MS fragment assignments that support the
proposed structure.
Figure 2: Flexible data visualization. The user can quickly switch between line
plots and bar graphs of the selected metabolites for time course results and
inter-species comparison.
Figure 3: Summary tables. Concise summary tables of all selected metabolites
across multiple samples are automatically generated in MetabolitePilot™ Software.
The user can display presence or absence (top) and the actual area counts of MS or
UV data (bottom).
84
Correlation reports are automatically generated, and all tables
and charts can be easily copied and pasted from the workspace
for preparing presentations.
RUO-MKT-01-1583-A
Figure 4: Integrated MS/MS interpretation. Directly within the results workspace,
structure editing and automatic fragment assignment can be performed for a
proposed metabolite. Fragments are assigned and highlighted on the
chemical structure.
MetabolitePilot Software has a powerful MS/MS fragment
interpretation function integrated directly in the results workspace
(Figure 4). When reviewing potential metabolites, the scientist can
edit structures and perform automatic fragment assignment. Both
the structure and fragment assignments are saved in the results
table and are directly accessible in data review. Integrated reports
can be generated automatically.
Fragment assignments are automatically highlighted on the
structure when the fragment is selected. The software also
automatically shows possible neutral losses from the selected
fragment to show its relationship to other product ions in
the MS/MS spectrum. A score is assigned to each fragment
assignment to aid in data review. The MS/MS spectrum can be
automatically annotated with fragment structures and elemental
compositions (Figure 4). Due to the high resolution and mass
Conclusions
New advanced functions in MetabolitePilot Software 1.5 help increase
the efficiency of data analysis and interpretation.
Multiple sample correlation goes beyond traditional single
sample/control processing to enable comparison of metabolites
across multiple samples easily and without manual data manipulation
in separate software.
Integrated MS/MS fragment interpretation improves the efficiency
of the most time-consuming aspect of metabolite data analysis by
performing fragment assignments on a proposed structure in a single,
integrated workspace throughput sample preparation and
fast chromatography.
•
•
•
DEFINITIVE METABOLITE ID
DEFINITIVE METABOLITE ID
For Research Use Only. Not for use in diagnostic procedures.
References
Rapid Metabolite Identification Using MetabolitePilot™ Software”, AB SCIEX Technical Note,
Publication 2490211- 01.
1“
2
R. J. Mortishire-Smith et al, “Evaluation of Chemically Intelligent Acquisition and Processing
Tools as a Platform for Discovery Metabolite Identification,” TP 222,The 59th ASMS
Conference on Mass Spectrometry and Allied Topics, June 5 – 9, 2011, Denver, CO.
3
To download a trial version of MetabolitePilot™ software, please visit: http://www.absciex.
com/Products/Software/MetabolitePilot-Software
Figure 5: Results workspace. Metabolite structures are an integral part of the results
table and are displayed automatically when a metabolite is selected.
DRUG METABOLISM
www.absciex.com
www.absciex.com
DRUG METABOLISM
85
Your success is our success
We take it personally
AB SCIEX internal expertise
As an AB SCIEX customer you have access to an excellent customer support organization.
Wherever you are, we’re there with you as a trusted partner to answer questions, provide solutions,
PHARMA BUSINESS UNIT
Suma Ramagiri
and maximize lab productivity.
Gary Impey
Joseph Fox
Debadeep Bhattacharya
RESEARCH APPLICATIONS
Yves LeBlanc
Larry Campbell
RESEARCH HARDWARE AND SOFTWARE
Bruce Thomson
Our customer support organization has access to the latest product updates, software revisions,
methods and repair procedures to make sure that you stay on top of your game.
When you have questions, we have answers.
Learn more at www.absciex.com/customersupport, or locate your local account representative at
www.absciex.com/contactus
Bruce Collings
Igor V Chernushevich
Bradley Schneider
Tom Covey
APPLICATION SUPPORT
Elliot Jones
Jeff Miller
Keith Goodman
Carmai Seto
Anoop Kumar
Kerong Zhang
Vicki Gallant
For Research Use Only. Not for use in diagnostic procedures.
© 2014 AB SCIEX. The trademarks mentioned herein are the property of AB Sciex Pte. Ltd. or their respective owners. AB SCIEX™ is being used under license.
9870414-01 07/2014 RUO-MKT-01-1583-A
Headquarters
500 Old Connecticut Path | Framingham, MA 01701 USA
Phone 508-383-7700
www.absciex.com
International Sales
For our office locations please call the division
headquarters or refer to our website at
www.absciex.com/offices
Your success is our success
We take it personally
AB SCIEX internal expertise
As an AB SCIEX customer you have access to an excellent customer support organization.
Wherever you are, we’re there with you as a trusted partner to answer questions, provide solutions,
PHARMA BUSINESS UNIT
Suma Ramagiri
and maximize lab productivity.
Gary Impey
Joseph Fox
Debadeep Bhattacharya
RESEARCH APPLICATIONS
Yves LeBlanc
Larry Campbell
RESEARCH HARDWARE AND SOFTWARE
Bruce Thomson
Our customer support organization has access to the latest product updates, software revisions,
methods and repair procedures to make sure that you stay on top of your game.
When you have questions, we have answers.
Learn more at www.absciex.com/customersupport, or locate your local account representative at
www.absciex.com/contactus
Bruce Collings
Igor V Chernushevich
Bradley Schneider
Tom Covey
APPLICATION SUPPORT
Elliot Jones
Jeff Miller
Keith Goodman
Carmai Seto
Anoop Kumar
Kerong Zhang
Vicki Gallant
For Research Use Only. Not for use in diagnostic procedures.
© 2014 AB SCIEX. The trademarks mentioned herein are the property of AB Sciex Pte. Ltd. or their respective owners. AB SCIEX™ is being used under license.
9870414-01 07/2014 RUO-MKT-01-1583-A
Headquarters
500 Old Connecticut Path | Framingham, MA 01701 USA
Phone 508-383-7700
www.absciex.com
International Sales
For our office locations please call the division
headquarters or refer to our website at
www.absciex.com/offices