Role of Metabolite Elucidation in Support of
Drug Discovery and Development: Strategy
and Techniques
Ala F. Nassar
Today’s Topics
•
•
•
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Introduction
Utilization of in-silico methods and automation
techniques for rapid metabolite elucidation
Online H-D exchange method
Presentation of technique capable of detection
down to 20 cpm 14C peaks
Using stable isotopes for metabolite elucidation
Case studies
Questions for Drug Metabolism Scientists
• What are the chemical reactions involved in the
•
•
•
•
metabolism of foreign compounds?
Where do these reactions take place in the body?
Which enzyme systems catalyze the metabolism of
foreign compounds?
What are the biochemical mechanisms of these
processes?
What are the biological consequences of xenobiotic
metabolism?
Outcome
Guiding drug candidate selection by taking advantage of
metabolic reactions to design more effective and safer drugs
Typical Screening Cascade
In Vitro Enzyme Assay
Cell Based Assay
Selectivity
In parallel
or
In series
Exposure screening
Microsomal stability
Metabolites ID
IV/PO PK studies
CYP inhibition screening
Caco-2 permeability
solubility
Expanded PK (definitive, TK, formulations, etc.)
Chronic efficacy model
Acute in vivo efficacy model
Select Drug Candidate
Techniques for ADME Studies
•
•
•
•
•
•
•
•
Microsomal Stability
Hepatocyte Stability
Caco-2 Permeability
Metabolic Profiling
Protein Binding
Whole Body Autoradiography
P450 Inhibition and Induction
Pharmacokinetic Analysis
•
Metabolite Elucidation
Significance of Metabolite Elucidation
• Assistance with chemical synthesis efforts to block
or enhance metabolism
• Prediction of metabolites likely to be formed in
•
•
•
•
vivo
Determination of metabolic differences between
species
Identification of potential pharmacologically-active
or -toxic metabolites
Synthesis of metabolites for toxicology testing
Prediction of drug-drug interactions
Method for Characterization of Metabolites in Drug
Discovery Utilizing in-silico Prediction, GENESIS
Workstation and QTOF-MS
The automated assay system consists of:
Pallas MetabolExpert software to predict possible
metabolites
Robotic liquid handler (Genesis workstation) to
generate and process samples
QTOF-MS coupled with liquid chromatography to
analyze samples
MetaboLynx software to find potential metabolites
Exact mass measurement to help for Met ID
Advanced Chemistry Development/MS (ACD/MS)
software to predict hypothetical metabolite chemical
structures
SELECTED COMPOUNDS TO EXAMINE NOVEL
METHODS/TOOLS FOR MET ID
O
OH
N
H
O
N
Dextromethorphan
Propranolol
NH
O
OH
Alprenolol
Prediction of possible metabolites for propranolol using
Pallas MetabolExpert 10.0 software
N-dealkylation
Conjugation
O
N
N-oxidation
O
Aromatic
Oxidation or
Epoxidation
Propranolol
O-dealkylaton
Prediction of possible metabolites for propranolol using
Pallas MetabolExpert 10.0 software
O
O
O
O
O
N
O
N
N
O
O
O
O
O
O
N
O
O
N
O
N
O
O
O
O
O
N
O
O
N
O
N
O
O
O
Propranolol
O
O
N
O
O
O
O
O
N
N
N
N
O
O
O
O
O
O
O
O
TOF-MS/MS spectrum of (A) dextromethorphan and (B) dextromethorphan-M1
213
-2H
-C3H7N
215
199
-4H
147
-2H
201
-C3H7N
-C3H9N
198
OH
O
N
N
121
-CH2O
-CH2
173
107
91
159
159
-H2
-H2
171
[M+H]+,
157
[M+H]+,
m/z 272
145
-C2H2
133
m/z 258
TOF MSMS 272.00ES+
171.1
100
-CH2
147.1
A
213.1
%
159.1
198.1
121.1
272.2
135.1
91.1
181.1
0
TOF MSMS 258.00ES+
157.1
100
B
133.1
199.1
%
145.1
107.1 121.1
0
80
100
120
258.2
171.1 185.1
140
160
180
200
220
240
260
m/z
280
TOF-MS/MS spectra of (A) propranolol and (B) propranolol-M1
98
218
-H2O
-H2O
98
183
234
-H2O
-H2O
OH
116
N
116
H
199
OH
N
H
O
O
157
-2H
129
145
155
-C3H7
OH
-C3H7
165
171
181
[M+H]+, m/z 276
[M+H]+, m/z 260
TOF MSMS 260.00ES+
116.1
100
173
-2H
155.1
183.1
A
%
98.1
129.1
165.1
260.2
141.1
86.1
218.2
0
TOF MSMS 276.00ES+
116.1
100
173.1
199.1
B
%
98.1
145.1
86.1
181.1
276.2
161.1
234.2
128.1
1
80
100
120
140
160
180
200
220
240
260
m/z
280
ACD/MS software as efficient tool to help in
assigning potential metabolite chemical structure
The ACD/MS software was used to help determine the chemical structures
for the metabolites. We performed the following steps to produce a potential
(hypothetical) metabolite chemical structure for each of our test compounds:





Generate the MS/MS spectrum for each metabolite;
Attach the corresponding potential (hypothetical) structure(s) to it;
Run Autoassign;
Use ACD/MS software to provide a score for percent of spectrum
assignment;
These scores then guided us to the most likely form for the potential
(hypothetical) chemical metabolite structure.
To speed up the process, we ran autoassign in batch mode on the various
metabolite possibilities for each particular molecule. We were able to increase
the intelligence of the process by using accurate mass data.
Mass accuracy measurements of selected drugs and their metabolites,
the exact mass difference between each metabolite and parent drug
Substrate
Molecular
Formula
Mass, m/z
Theoretical
Mass
Measured
Massa
Mass
Error,
Da
SD,
Da
ppm
Exact Mass
Differencea,b
Dextrom1
M-1
M-2
M-3
C18H26NO
C17H24NO
C16H22NO
C17H24NO
272.2014
258.1858
244.1706
258.1858
272.2012
258.1868
244.1707
258.1862
0.0002
0.0010
0.0001
0.0004
0.0008
0.0002
0.0010
0.0013
0.7
3.8
0.4
1.5
-14.0146
28.0307
14.0152
Propranolol
M-1
M-2
M-3
C16H22NO2
C16H22NO3
C16H22NO3
C16H22NO3
260.1651
276.1600
276.1600
276.1600
260.1649
276.1601
276.1602
276.1602
0.0002
0.0001
0.0002
0.0002
0.0005
0.0009
0.0009
0.0006
0.7
0.3
0.7
0.7
-15.9950
15.9951
15.9951
Alprenolol
M-1
M-2
C15H24NO2
C15H24NO3
C15H24NO3
250.1807
266.1756
266.1756
250.1808
266.1763
266.1752
0.0001
0.0007
0.0004
0.0007
0.0015
0.0003
0.3
2.6
1.5
-15.9956
15.9945
1
Dextromethorphan
Avg. of 5 injections.
b
Exact mass difference between each metabolite and parent drug, theoretical mass of CH2 is 14.0157
Da, C2H4 is 28.0313 Da, and oxygen is 15.9949 Da.
a
Pallas software was useful in predicting the possible
metabolic fate of the compounds examined
Compound
Molecular
Formula
Pallas
ACD
Dextromethorphan
Dextromethorphan-M1
Dextromethorphan-M2
Dextromethorphan-M3
C18H26NO
C17H24NO
C16H22NO
C17H24NO
-Yes
Yes
Yes
n=7
-88
98
88
Propranolol
Propranolol-M1
Propranolol-M2
Propranolol-M3
C16H22NO2
C16H22NO3
C16H22NO3
C16H22NO3
-Yes
Yes
Yes
-82
86
71
n=15
Alprenolol
Alprenolol-M1
Alprenolol-M2
C15H24NO2
C15H24NO3
C15H24NO3
-Yes
Yes
n=11
-58
61
Proposed metabolic pathways of dextromethorphan in hepatic
microsomal incubations based on MS/MS and ACD data
O
N
O-demethylation
N-demethylation
Dextromethorphan
m/z 272
OH
O
N
N
H
O-demethylation
Methoxymorphinan, M-3
[M+H]+ m/z 258
N-demethylation
OH
N
H
Hydroxymorphinan, M-2
[M+H] + m/z 244
Dextrorphan, M-1
[M+H]+ m/z 258
Proposed metabolic pathways of propranolol in hepatic microsomal
incubations based on MS/MS and ACD data
OH
N
H
O
Propranolol
[M+H]+ m/z 260
[O]
[O]
[O]
OH
OH
OH
N
N
N
H
H
H
O
O
O
HO
OH
OH
Monohydroxy propranolol, M-1
[M+H]+ m/z 276
Monohydroxy propranolol, M-2
[M+H]+ m/z 276
Monohydroxy propranolol, M-3
[M+H]+ m/z 276
Proposed metabolic pathways of alprenolol in hepatic
microsomal incubations based on MS/MS and ACD data
NH
O
OH
Alprenolol
[M+H]+ m/z 250
[O]
[O]
OH
HO
NH
O
OH
Monohydroxy-Alprenolol, M-1
[M+H]+ m/z 266
NH
O
OH
Monohydroxy-Alprenolol, M-2
[M+H]+ m/z 266
CONCLUSIONS
 Pallas is a useful software tool to predict possible metabolites
 The QTOF mass spectrometer generates accurate MS data
(high resolution) which can rapidly identify metabolites
when combined with MetaboLynx software
 MetaboLynx
software is useful to automatically find
potential metabolites, avoiding time-consuming data analysis
 ACD software is useful to assist in the mass spectral data
interpretation, leading to tentative structures for the
proposed metabolites in drug discovery
 The major metabolites were found in rat and human liver
microsomal preparations and were in good agreement with
previously published results
Hydrogen-deuterium Exchange and
QTOF-MS for Metabolite Elucidation in
Drug Metabolism
H-D exchange methods are useful for determination of:
• N- or S-oxide formation and mono-
hydroxylation
• Conjugation such as glucuronidation
• Dehydrogenation or dealkylation
Prediction of possible metabolites for
Nimodipine using Pallas software
9
1.
2.
3.
4.
5.
6.
7.
8.
9.
Cleavage
Cleavage
Cleavage
Hydroxylation
Hydroxylation
Cleavage
Cleavage
Hydroxylation
Cleavage
O+
N
O
3
O
O
8
O
7
O
O
N
1
6
H*
2
4
5
Exchange of labile hydrogens in nimodipine and
metabolites formed in-vitro
Compound
Labile
Hydrogen
Mass
1
[MH +H]+, m/z
Nimodipine
M-1
M-2
M-3
M-4
M-5
1
1
2
1
2
0
419
359
435
403
405
417
1
MH=the molecular weight in H2O
MD=the molecular weight in D2O
2
Mass [MD2+D]+, m/z
predicted measured
421
361
438
405
408
418
421
361
438
405
408
418
Proposed metabolic pathways of nimodipine in hepatic microsomal incubations in H2O
O
+
N
O
O
O
O
O
O
N
H
CH2OH
[MH+H]+ m/z 435
M-2
O
O
+
N
O
O
+
N
O
O
O
O
HO
O
O
O
O
N
N
O
H
H
+
[MH+H]+ m/z 419
Nimopdipine
N
O
HO
O
O
O
N
C
H
O
O
O
H
O
[MH+H]+
+
N
[MH+H]+ m/z 405
M-4
O
O
m/z 359
M-1
+
N
O
O
O
O
H
O
O
O
O
O
O
N
N
[MH+H]+ m/z 417
M-5
[MH+H]+ m/z 403
M-3
Proposed metabolic pathways of nimodipine in hepatic microsomal incubations in D2O
O
+
N
O
O
O
O
O
O
N
D
CH2OD
[MD+D]+ m/z 438
M-2
O
O
+
N
O
O
+
N
O
O
O
O
DO
O
O
O
O
N
D
+
[MD+D] m/z 421
Nimopdipine
N
O
O
DO
O
O
O
O
N
C
H
O
O
O
H
O
[MD+D]+
+
N
D
[MD+D]+ m/z 408
M-4
+
N
O
m/z 361
M-1
+
N
O
O
O
O
DO
O
O
O
O
O
N
N
[MD+D]+ m/z 418
M-5
[MD+D]+ m/z 405
M-3
CONCLUSIONS
•
One advantage of the H-D exchange method is that, with
LC-MS/MS, it offers an easy estimation of the number of
labile hydrogen atoms in such groups as -OH, -NH, -NH2
and -COOH
•
This number is useful in comparing the metabolite structure
with that of the parent drug to determine the presence or
absence of the above groups
•
H-D exchange experiments have facilitated structural
elucidation and interpretation of fragmentation processes
as well
•
Our results indicated that this method should be
particularly desirable for identification of metabolites
produced by dehydrogenation, oxidation, and dealkylation
NOVEL APPROACH TO PERFORMING
METABOLITE IDENTIFICATION IN DRUG
METABOLISM
AIMS
• Validate capability of on-line radioactivity
detector coupled with MS to measure
radiolabeled compounds
• Enhance the sensitivity of radioisotope
measurement for metabolite identification
LC-ARC-MS-FC System
PC
Sampler
Column
Agilent 1100
Pump
StopFlow
Controller
Packard 500
Detector
Waste
LCQ MS
Cocktail
Fraction Collector
Laptop PC
ARC Data System
ARC Control Lines
LCQ Control Lines
Linearity of DynamicFlow Radioactivity Detection
700
y = 1.1213x - 50.702
2
R = 0.996
Peak Area
Peak Area
(DPM)
600
500
400
Detected DPM
Linear (Detected DPM)
300
200
100
0
0
200
400
Applied DPM
DPM
Applied
600
800
HPLC-MS chromatograms of [14C]dextromethorphan following incubation with
HLM in the presence of NADPH showing M-1, M-2, M-3 and dextromethorphan
RT: 0.00 - 32.00 SM: 15G
NL: 1.10E6
m/z= 273.00-275.00 F:
ITMS + c ESI Full ms [
200.00-300.00] MS
HLM-C14-60min
20.52
100
50
3.16 3.86
0.44
0
100
7.16
9.77 12.29 14.05 15.82 16.71
12.40
19.48
21.03 22.17 24.58 26.46 28.23
31.59
NL: 4.28E5
m/z= 257.95-258.52 F:
ITMS + c ESI Full ms [
200.00-300.00] MS
HLM-C14-60min
12.34
12.50
50
0.16
0
100
3.01 4.02 5.95 7.45
9.31
12.58 15.28
12.22
19.09 21.76 22.03 22.26 25.08 26.97 28.38
31.58
NL: 4.54E4
m/z= 243.00-245.00 F:
ITMS + c ESI Full ms [
200.00-300.00] MS
HLM-C14-60min
13.35
50
3.03
0
100
3.13
3.35
7.41 9.40 11.24 12.78
13.88
21.26 22.15 23.85 25.54 27.85
21.89
17.76 18.60
31.58
NL: 5.91E5
m/z= 260.00-262.00 F:
ITMS + c ESI Full ms [
200.00-300.00] MS
HLM-C14-60min
50
3.01 3.71 5.31
0.44
0
0
2
4
6
8.33 9.74 12.15 12.41
8
10
12
22.26
15.34 16.79 18.91 19.85
14
16
18
Time (min)
20
22
23.39 25.83 28.03
24
26
28
31.58
30
LC-MS spectra of [M+H]+ m/z 274, nonmetabolized [14C]dextromethorphan, (A) MS2, (B) MS3
and (C) MS4 following incubation of [14C]dextromethorphan with HLM
dex274 #237-272 RT: 0.63-0.72 AV: 36 NL: 1.58E6
F: ITMS + c ESI Full ms2 274.00@50.00 [ 75.00-300.00]
217.12
1400000
1200000
Intensity
1000000
800000
600000
215.14
400000
149.03
200000
0
83.00 90.99
80
122.97
148.26
120
140
109.05
100
161.06
175.07
160
201.11 214.32
180
200
231.16
220
243.12
256.23
240
274.22
260
280
300
m/z
dex274 #383-419 RT: 1.09-1.22 AV: 37 NL: 5.88E5
F: ITMS + c ESI Full ms3 274.00@50.00 217.00@50.00 [ 55.00-300.00]
148.98
550000
500000
450000
Intensity
400000
350000
300000
175.02
250000
200000
161.00
150000
100000
122.94
50000
136.99
68.92
0
60
80.93
80
92.91
100
120
189.04
163.01
106.97
140
160
180
m/z
198.13
200
220
240
260
280
300
dex274 #701-736 RT: 2.55-2.70 AV: 36 NL: 3.56E4
F: ITMS + c ESI Full ms4 274.00@50.00 217.00@50.00 149.00@50.00 [ 50.00-150.00]
90.88
35000
30000
Intensity
25000
20000
15000
120.97
10000
131.98
5000
118.94
92.90
54.88
0
50
106.91
94.99
60
70
80
90
100
m/z
110
114.97
130.97
129.82
120
130
133.99
149.04
140
150
201
-CH2
215
-2H
217
-C3H7N
O
*
CH3
121
N
123
175
-*CH2O
123
149
-C2H2
[M+H]+, m/z 274
[14C]dextromethorphan
*=C14
-2H
91
-C2H2
Proposed metabolic pathways of dextromethorphan in hepatic
microsomal incubations
O
*
CH3
N
N-demethylation
O
O-demethylation
Dextromethorphan
[M+H]+ m/z 274
OH
*=C14
*
CH3
N
Dextrorphan
[M+H]+ m/z 258
N
H
Methoxymorphinan
[M+H]+ m/z 260
O-demethylation
OH
N
H
Hydroxymorphinan
[M+H]+ m/z 244
N-demethylation
CONCLUSIONS

This system detects 14C peaks down to 6 cpm, about 20 times more
sensitively than commercially available flow-through radioactivity
detectors

The LC-ARC on-line stop-flow method detects 3H peaks up to 10
times more sensitively than commercially available flow-through
radioactivity detectors

On-line mass spectrometer data were acquired

Using this method, it is possible to generate high resolution
radiochromatograms, accurately measure volatile metabolites which
fraction-collection methods are not even able to detect, and acquire
mass spectra on-line

An important safety benefit is that, by using this method, we reduced
sample injection size, thus reducing radioactivity exposure and wastes
Stable Isotope Approach for Metabolite
Elucidation in Drug Metabolism
Case Studies
Considerations to Enhance Metabolic Stability.
 One of the most important keys to successful drug design and development is a
process of finding the right combination of multiple properties such as activity,
toxicity and exposure. Optimize these three properties for drug candidates, and thus
their suitability for advancement to development.
 The responsibility of the drug metabolism scientist is to optimize plasma T1/2
(clearance compound), drug/metabolic clearance, metabolic stability, and the ratio
of metabolic to renal clearance.
 Another concern is to minimize or eliminate the following:
• gut/hepatic-first-pass metabolism,
• inhibition/induction of drug-metabolizing enzymes by metabolites,
• biologically active metabolites,
• metabolism by polymorphically expressed drug-metabolizing enzymes
• formation of reactive metabolites.
Advantages of Enhancing Metabolic Stability
 Increased bioavailability and longer half-life, which in turn should allow lower
and less frequent dosing thus promoting better patient compliance.
 Better congruence between dose and plasma concentration, thus reducing or
even eliminating the need for expensive therapeutic monitoring.
 Reduction in metabolic turnover rates from different species which, in turn,
may permit better extrapolation of animal data to humans.
 Lower patient-to-patient and intra-patient variability in drug levels, since this is
largely based on differences in drug metabolic capacity.
 Diminishing the number and significance of active metabolites and thus
lessening the need for further studies on drug metabolites in both animals and
man.
Strategies to Enhance Metabolic Stability
 In general, metabolism can be reduced by incorporation of stable functions
(blocking groups) at metabolically vulnerable sites. Substrate structure activity
relationships of metabolizing enzymes have to be accommodated within the
structure activity relationships of the actual pharmacological target.
 The following strategies have been used:
• Deactivating aromatic rings towards oxidation by substituting them with strongly
electron withdrawing groups (e.g., CF3, SO2NH2, SO3-).
• Introducing an N-t-butyl group to prevent N-dealkylation.
• Replacing a labile ester linkage with an amide group.
• Constraining the molecule in a conformation which is unfavorable to the metabolic
pathway, more generally, protecting the labile moiety by steric shielding
• The phenolic function has consistently been shown to be rapidly glucuronidated.
Thus, avoidance of this moiety in a sterically unhindered position is advised in any
compound intended for oral use.
Strategies to Enhance Metabolic Stability (cont’d)
• Avoidance of other conjugation reactions as primary clearance pathways, would
also be advised in the design stage in any drug destined for oral usage.
• Sometimes the best strategy is to anticipate a likely route of metabolism and
prepare the expected metabolite if it has adequate intrinsic activity. For example,
often N-oxides are just as active as the parent amine, but won't undergo further Noxidation.
Examples from literature to enhance metabolic stability in
the molecular design
Reduce the overall lipophilicity (logP, logD) of the structure
H
N
O
O
O N
O
N
N
H
O
N
H
O
O
EC50 = 0.078mM, clogP = 2.07, C7hr = 0.012mM
F
F
O
H
N
EC50 = 0.058mM, clogP = 0.18, C7hr = 0.057mM
O
O N
O
N
H
N
O
N
H
O
O
Dragovich, P. et al (2003). Journal of Medicinal Chemistry, 46(21), 4572-4585.
Remove or block the vulnerable site of metabolism (Benzylic oxidation)
N
Br
N
O
Ki = 66 nM, AUC 0-6h = 40 ng/ml hr
O
O
N
N
_
O
Br
N
N
N
Br
N
N
O
O
Ki = 2.1 nM, AUC 0-6h = 6500 ng/ml hr
Ki = 2 nM, AUC 0-6h = 1400 ng/ml hr
Palani, A. et al (2001) Journal of Medicinal Chemistry, 44(21), 3339-3342.
+
Remove or block the vulnerable site of metabolism (Allylic oxidation)
N
NH2
N
IC50 = 0.06 mg/ml, Cmax = 14-140 ng/ml
O S O
N
NH2
N
O S O
IC50 = 0.02 mg/ml, Cmax = 70-300 ng/ml
Victor F et al (1997). Journal of medicinal chemistry, 40(10), 1511-8.
Remove or block the vulnerable site of metabolism (Glucuronidation)
Effect of linker
OH
O
O
O
NH2
N
NH2
N
OH
F
O
F
UDPGA rate (nmol/min/mg protein) = 0.19, t1/2 = 4.7 hr
UDPGA rate (nmol/min/mg protein) = 0.05, t1/2 = 5.5 hr
Effect of template
OH
OH
O
S
NH2
N
O
F
NH2
N
O
F
UDPGA rate (nmol/min/mg protein) = 0.05, t1/2 = 5.5 hr
UDPGA rate (nmol/min/mg protein) = 0.012, t1/2 = 14.5 hr
Effect of stereochemistry
OH
OH
O
O
O
NH2
N
O
F
UDPGA rate (nmol/min/mg protein) = 0.02, t1/2 = 7.7 hr
O
NH2
N
O
F
UDPGA rate (nmol/min/mg protein) = 0.01, t1/2 = 8.7 hr
Bouska J J. et al (1997) Drug metabolism and disposition: biological fate of chemicals, 25(9), 1032-8.
Examples to improve PK properties through
structural modification of drug candidates
Minimize First-pass effect/prodrug approach
•Oral dosage of propranolol (Hasegawa et al 1978) produces a low bioavailability and a
wide variation from patient to patient when compared to intravenous administration; this
difference is attributed to first-pass elimination of the drug.
•Hemisuccinate ester of propranolol was selected as a potential prodrug with the hypothesis
that propranolol hemisuccinate ester administration would avoid glucuronide formation
during absorption and subsequently be released in the blood by hydrolysis.
COOH
HO
Propranolol
+
O
Hydrolysis
O
OH
N
H
N
H
O
O
COOH
Glucuronidation
O
Propranolol
AUC 0-6 = 132 ng/ml.h
Hemisuccinate ester of propranolol
AUC 0-6 = 1075 ng/ml.h
Examples to improve PK properties through
structural modification of drug candidates (cont’d)
Half-life
•ABT-418, an analogue of (S)-nicotine in which the pyridine ring is replaced by the 3methyl-5-isoxazole moiety, has been shown to possess cognitive-enhancing and anxiolyticlike activities in animal models with an improved safety profile compared to that of nicotine
(Lin et al. 1997).
•One shortcoming of ABT-418 was its very poor bioavailability (%F = 1.2), with a short
plasma half-life (t1/2 = 0.21 h). Research on structural modification led to the identification
of ABT-089, 2-Methyl-3-(2(S)-pyrrolidinylmethoxy)pyridine, with a vastly improved oral
bioavailability (%F = 61.5) with t1/2 = 1.6 h.
O
N
N
N
H
O
N
5 pyrollidine ABT-418
t1/2 = 0.2 h
ABT-089
t1/2 = 1.6 h
Conclusions
 In-silico and in vitro techniques are available to screen compounds for key
ADME characteristics.
 Structural information on metabolites is a great help in enhancing as well as
streamlining the process of developing new drug candidates.
 By improving our ability to identify both helpful and harmful metabolites,
suggestions for structural modifications will optimize the likelihood that other
compounds in the series are more successful.
 Structural modifications to solve a metabolic stability problem may not
necessarily lead to a compound with an overall improvement in PK properties.
 Solving metabolic stability problems at one site could result in the increase in
the rate of metabolism at another site, a phenomenon known as metabolic
switching. Further, reduction in hepatic clearance may lead to increased renal or
biliary clearance of a parent drug or inhibition of one or more drug-metabolizing
enzymes. Therefore, it is advisable that in vitro metabolic stability data be
integrated with other ADME screening.
Conclusions (cont’d)
 An accurate measurement of the pharmacokinetic parameters and a good
understanding of the factors that affect the pharmacokinetics will guide drug
design.
 High metabolic liability usually leads to poor bioavailability and high
clearance, and formation of active or toxic metabolites will have an impact on
the pharmacological and toxicological outcomes.
 Drug candidates should have little or none of the following:
•gut/hepatic-first-pass metabolism
•inhibition/induction of drug-metabolizing enzymes
•biologically active metabolites
•metabolism by polymorphically expressed drug-metabolizing enzymes
•formation of reactive metabolites
•Also, it is important to have the most desirable plasma half-life and ratio of
metabolic to renal clearance.