A Roadmap for
PAT Implementation in
Pharmaceutical Manufacturing
Robert M. Leasure
Principal Scientist
Site PAT Champion
Pfizer Global Manufacturing
7000 Portage Road, PORT-91-201
Kalamazoo, MI 49001
(269) 833-6198
-1-
Presentation Outline
 Provide some Definitions about PAT
•
But in the process more Questions will be asked than definitions provided.
•
Asking the right Questions provides the framework for successful implementations.
 Site perspective of a PAT program
•
Project Selection
•
Resource Allocation – from site and center support
•
Steps for Implementation
 Examples of PAT Implementations in Kalamazoo Manufacturing Ops
•
Drug Product
Parental Sterile Suspension - improved content uniformity
•
Drug Product
Dissolution Monitoring of Active during pH adjustment
•
API Operations
Solvent Recovery – improved yield from timely fraction determination.
-2-
Definitions and Questions
What is PAT?
Process Analytical Technologies
Things that come to mind…..
 Probes in Tanks
Analyzers in Plant
 Automation
 Process Data (lots of it)
Questions that come to mind…..
 Where are you going to stick
that probe?
 How are you going to validate
that system?
 What are you going to do with
that data?
-3-
What is PAT?
The answer is multivariate and transient.
It depends on who is asking the question,
and who is giving the answer.
Technologists
Managers
$$$
Support Groups
Quality and
Regulatory Groups
IT,
Engineering, Maintenance
-4-
What is (a)
PAT?
On-line
Bona fide
On-line
PAT System
Fiber-Optic
pH Probe
Probe
Near-Infrared
Analog
Spectrometer
Recorder
Analytical
Instrument
Feedback
Control
At-line
Off-line
Probe
Automation
Automation
Pfizer
Reactor
Control Room
vs.
Sample
Valve
Reactor
In-Plant Laboratory
-5-
FDA Guidance on PAT
FDA Guidance Document on PAT
Released in September 2004.
http://www.fda.gov/cder/guidance/6419fnl.htm
Ajaz S. Hussain, Ph.D.
Previously Deputy Directory Office of
Pharmaceutical Science, CDER, FDA
Key proponent for the use of PAT in
the pharmaceutical industry.
-6-
FDA Definition of PAT
 FDA Guidance – September 2004
PAT – A Framework for Innovative Pharmaceutical
Manufacturing and Quality Assurance
 Line 158:
“For the purposes of this guidance document, PAT is
considered to be a system for designing, analyzing,
and controlling manufacturing through timely
measurements (i.e., during processing) of critical
quality and performance attributes of raw and inprocess materials and processes with the goal of
ensuring final product quality.”
-7-
Who benefits
What is (a)
from
PAT?
(a) PAT?
The Users
Technologists
Managers
$$$
Support Groups
Quality and
Regulatory Groups
IT,
Engineering, Maintenance
1. Manufacturing Operations
2. R&D or Process Scientists
-8-
Where does PAT begin (and end)?
Involvement
Co-development or
Continuous Improvement Activities
R&D or
Manufacturing
Operations
Process Support
* Proceed with PATs
in development?
PAT Project Progression
"Early PAT"


Used to determine
Critical Process Parameters
Low cost / benefit ratio
"Late PAT"

Used to control the process

Requires formal validation
-9-
Why do PAT?
RFT
Well Controlled Process
Fundamental Goals
Improved quality.
Improved safety.
Cost savings.
Process Control
Process Knowledge
- 11 -
Continuous Quality Verification
Inputs
Cost
Schedule
Quality
Process
What is done on the
plant floor.
(Compliance)
Process Analytics
Action
Root Cause
Analysis
Well Controlled Process Model
D
Data
People
Equipment
Procedures
Materials
Metrics
Evaluation
Requirements
- 12 -
Use of PAT to Achieve RFT Benefits
 Reduce/eliminate deviations
 Improve customer service (product availability)
 Reduce cycle times (operational efficiency)
 Reduce inventory levels
 Reduce costs (reworks, resample, retesting, etc)
 Improve capacity utilization
 Improve compliance (reduce deviation reports)
 Improve assurance of quality
Reduced need for end product testing is a potential consequence of
RFT performance, but is not the direct goal of Pfizer’s PAT strategy.
- 13 -
Six Questions

What ?do you wantChemical
to measure?
or physical property.

How ?do you want toAnalytical
measure it?
technology.

Why ?do youProcess
want to measure
it?
Knowledge
or Process Control?

Where ?do youBefore,
want toduring,
measure?
or after a process step?

When ?do you wantSampling
to measurefrequency.
it?

Who ?will look at the results?
Validation…..
- 14 -
Considerations for Project Identification
 Is the process “broken”?
Are there unknown or unmeasured critical process parameters?
 How big is the problem?
What are the risks of non-conformance?
What is the cost of poor throughput?
 Where should the measurement be made?
At-line or On-line? (On-line is usually > 3x more $.)
Are there area classification requirements? i.e., Class I Div I
 How often should a measurement be made?
What are the process and instrument limitations?
 What decisions will be made with the data?
Does Quality Operations want to intimately know the process?
What are the Regulatory implications?
 Will implementation affect other processes?
What is the impact on Cleaning Validation and probe
material of construction compatibility?
- 15 -
PAT System Qualification
 PAT System Qualification and Method Validation
should be based on intended use of data.
Three Levels
Quality Impact
1. Development or Proof of Concept
No Impact
2. Information Only
Indirect Impact
3. Release Decisions
Direct Impact
 Validation or Commissioning and Qualification
must conform to applicable:
 Corporate Quality Standards
 Site Procedures
- 16 -
Quality Impact Assessments
 Process Knowledge
•
No Impact or Indirect Impact (validation perspective)
•
Short term study used to assess process variability,
and potential need for a permanent PAT
 Process Monitoring
•
Indirect Impact, requiring “Commissioning of Equipment”
•
More permanent implementation.
•
Monitors process to assure RFT, but not used for decision
making; i.e., registered or validated assay already exists.
 Process Control
•
Direct Impact, requiring “Qualification of Equipment”
•
Used for
-
Material Release or Parametric Release
-
GMP Decisions for Critical to Process Parameters (CPP)
-
Advanced Process Control
- 17 -
PAT Development Resources for Kalamazoo
Two main manufacturing operations:
Active Pharmaceutical Ingredients
• Fermentation Operations
• Chemical Operations
Drug Product
• Sterile Injectables
• Non-sterile Fluids and Ointments
Site Technology Groups
Kalamazoo Process
Technology (KPT)
Site PAT Group
Center Function Support
Product and Process
Technology (PPT)
Right First Time
(Black, Green, Yellow
Belts)
Process Analytical
Support Group (PASG)
- 18 -
Site Implementation Plan (SIMP)
 Updated annually, by PAT Champion.
 High level plan extending out 3 years.
 Approvals
•
Site Leadership Team (KLT) and KPT &PPT Management
•
US Area RFT Team Lead
•
PASG Implementation Team Lead
 Purpose
1. Track existing PAT projects
2. Identify potential new projects
3. Prioritize new and existing projects
4. Implementation Timing
5. Resource Allocation
- 20 -
Project Prioritization
3
1
Rank as a Percentage
10
8
3
4
3
10
76%
UV-ATR Hydrogenation Reaction Monitoring
API
9
7
8
7
6
7
5
8
74%
NIR Process T - Ylide formation
API
8
5
8
5
3
8
10
10
73%
NIR Steroide B - Reaction Monitoring
API
8
5
10
2
8
8
8
10
73%
UV-VIS Rinsate Cleaning Optimization
API
9
5
9
3
8
9
10
8
72%
DP-INJ
5
8
9
1
8
8
8
10
69%
OLGC SRD Distillation Monitoring
API
6
3
10
6
3
2
5
10
66%
Vial Headspace Analysis for Oxygen
DP-INJ
8
9
8
1
3
2
5
9
61%
API
3
4
8
10
5
3
5
5
60%
Turbidity Dissolution Endpoint
OLMS Ceplasporin Dryer Monitoring
(higher is less constraigned)
6
(>$300K = 1, <$10K = 10)
8
Implementatoin Cost
QO
(difficult = 1, simple = 10)
Raman ID of Incoming Raw Materials
Project
Business Area
Site Specific Criterion
1
Regulatory Constraints
1
Project Complexity
3
EHS Improvement
3
Improved Efficiency or
Process Improvement
2
Quality Improvement
2
Increased Process
Understanding
Weighting Factor:
- 21 -
Technology Development Process
SIMP
Site Implementation
Plan
PAT
Project
Ideas
Justification
review and
project
prioritization
Production
Quality Operations
EHS
Technology Groups
Automation
Engineering
PAT Champion
Tech
Report on
Lab POC
Studies
Lab
proof of
concept
PAT Champion
PASG
Tech Groups
Vendor
PAT
Project
Charter
Development
Plant POC
Report
CPA
(if needed)
Project
specific
team
organized
PAT Champion
Production
Quality Ops
EHS
Tech Groups
Automation
Engineering
Plant
proof of
concept
Project Team
PASG
Vendor
Decision
to
proceed
Project Team
Site Management
PASG
Adapted from an illustration by Seamus O’Neill (PASG, Ireland)
- 22 -
PAT ImplementationTeam
Implementation of a PAT requires input from a multi-disciplinary team.
PAT
Champion
Maintenance
RFT
Champion
Management
Manufacturing
Operations
Validation
Services
PAT
Project
Information
Technology
Automation
Tech Services
(KPT or PPT)
PASG
Engineering
R&D
(co-dev)
Environmental,
Health and Safety
Quality
Operations
Regulatory
- 23 -
GAMP Model for Instrument Qualification
Good Automated Manufacturing Practice
User
Requirements
Performance
Qualification
Functional
Specifications
Operational
Qualification
Design
Specifications
Installation
Qualification
Installation
- 24 -
Q
More
uestions
What are you going to do with the data?
 Is the information used for material release?
 Do components come into direct contact with product?
 Is there a GMP Impact?
 Is there a Regulatory Impact?
 Does the system affect product quality?
 What if the system fails?
 How should the data be archived?
 Etcetera
(ca. 14 questions for a system level impact assessment)
Really asking:
Is the PAT for Process Knowledge or Process Control ?
Answer:
Quality Impact Assessment
document
- 25 -
Implementation Process
URS
QIA
Quality
Impact
Assessment
Definitive
CPA
User
Requirements
Specifications
Capital Project
Approval
FDS
IQ/OQ
PQ
Functional
Design
Specifications
Installation and
Operation
Qualification
Performance
Qualification
Cost review,
justification,
Define
vendor
Requirements
selection,
and approval
PAT Team
PASG
Project Team
PASG
Vendor
FAT, SAT,
installation,
qualification
Vendor
Project Team
PASG
Validation Services
Application
verification
Production
Quality
PAT Champion
Lifecycle Docs
• Analytical Methods
• Operation SOPs
• Maintenance SOPs
• Training Docs
• Change Control
• Periodic Review
•Business Continuity
Plan
Routine
Ready for
Routine
Operation
Operation?
Cross Site
Learning
Adapted from an illustration by Seamus O’Neill (PASG, Ireland)
- 26 -
Example #1 – CU in a Sterile Suspension
 Application:
Drug Product
Sterile Aqueous Suspension
 Quality Impact:
No Impact, Process Knowledge
(product was not for sale)
 Objective:
Improved Content Uniformity
during later stages of filling operation.
 Project:
RFT and Continuous Improvement
Black Belt project to provide
suggested process changes for
improved content uniformity.
- 27 -
Drug Product – Sterile Injectable
 Parenteral Suspension
 Solid
•
Drug (20 - 150 mg/mL)
 Vehicle
•
Water (> 95%)
•
Surfactants
•
Preservative
 2 mL vial with 1.2 mL fill
- 28 -
Sterile Suspension Filling Operation
On-line Turbidity of
Bulk Suspension Recycle Loop
Off-line or At-Line
NIR Analysis
of Filled Vials
- 29 -
Potency vs. Amount Filled
Lot B
A
Lot
Off-line NIR
HPLC
HPLC
165
170
Potency (mg/mL)
165
160
160
155
155
150
150
145
145
RSD NIR = 0.44%
0.55%
RSD HPLC = 0.83%
0.49%
RSDNIR = 1.91%
3.04%
RSDHPLC = 2.57%
4.47%
140
0
20
40
60
80
100
Approximate
Approximate Percent
Percent Filled
Filled
 Filling operation is controlled within specifications, but there
is opportunity for improvement near the end of the batch.
- 30 -
At-Line NIR for Suspension Vial Analysis
 Foss NIRSystems Model 6500
•
Dispersive NIR spectrometer
•
fiber-optic probe
 Spinner - Sample Module
•
fiber-optic probe
•
in-house built accessory
 Vision® software
 Analysis time ~ 1 vial/min
 Non-destructive, Non-invasive
- 31 -
Sample Spinner Schematic
sample
needle bearing
sleeve holder
rotating gear
(w = 125 rpm)
fiber optic probe
45 °
mounting
bracket
- 32 -
Apparent Concentration (mg/mL)
Effect of Spin-rate
on Apparent Concentration
0 rpm
250
230
210
190
25 rpm
170
50 rpm
150
125 rpm
130
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Time (min)
- 33 -
1.4
Absorbance log(1/R)
1.2
Potency (mg/mL)
Raw Near-IR Spectra
200
187
168
175
150
150
131
125
114
100
Sample
1.0
0.8
0.6
0.4
0.2
0.0
1100
1300
1500
1700
1900
2100
Wavelength (nm)
- 34 -
1st Derivative Spectra
0.20
0.12
0.15
0.10
0.04
0.05
0.00
1250
st
1 Derivative
0.08
1300
1350
1400
1450
1500
0.00
0.015
-0.05
0.010
0.005
-0.10
1100
0.000
1300
1500
1700
Wavelength (nm)
1900
2100
-0.005
1600 1650 1700 1750 1800 1850
-0.010
- 35 -
Near-IR Calibration
200
NIR Potency
(mg/mL)
190
Partial Least Squares Model
2 factors, 1st derivative, 1650-1800 nm
180
170
160
150
140
130
Training Set (SEC = 1.28 mg/mL, R = 0.99)
Test Set
(SEP = 1.40 mg/mL, R = 0.99)
120
110
100
100
110
120
130
140
150
160
170
180
190
200
Lab Potency (mg/mL)
- 36 -
Optek Turbidity Sensor
1. Sensor Body
2. Windows
3. NIR Filter
4. Photo Diode
5. Optics Module
6. Tungsten Lamp
- 37 -
Calibration of On-line Turbidity Sensor
Calculated Potency (mg/mL)
165
Calcuated Potency = 116.6 + 12.95 (OpSig)
R² = 0.995
160
A/D Converter (volts)
concentrated
suspension with known
amounts of vehicle.
157
153.7
3.0
 Incrementally dilute a
162
158.3 mg/mL
152
148.5
2.5
147
140.9
2.0
142
137
1.5
132
1.0
Potency (mg/mL)
3.5
127
0.5
122
0.0
117
0
10
20
30
Time (min)
40
50
155
150
 Correlate calculated suspension
145
potency with turbidity sensor
response.
140
135
1.5
2.0
2.5
3.0
3.5
Optek Signal (volts)
- 38 -
DOE Study using On-line Turbidity
 RFT Black Belt Project to improve Content Uniformity by optimizing
filling parameters.
 6 factor DOE study was conducted varying mixing time, mixing power,
recirculation flow-rate, etc.
Tommy Garner
- 39 -
DOE Results
 Bottom mixer has minimal contribution to mixing.
- 40 -
DOE Results continued
 Mixer power is critical for consistent CU.
- 41 -
Improved Filling Process
 Proposed process change: leave mixer on longer.
 Three lots demonstrated no dip and no tail at end of fill.
- 42 -
Advantages offered by On-Line Turbidity
 Improved temporal sampling resolution.
 Cost savings, by reducing or eliminating the need to
perform off-line analysis by NIR or HPLC.
Note: HPLC analysis by routine labs is ca. $100/analysis.
 Eliminated error of taking “grab” samples for off-line
analysis. This was found to be significant, if the sampling
line is not properly configured, due to settling.
 Time savings - ability to perform several parts of the DOE
during the same run, i.e., ability to see when system has
become perturbed or equilibrated.
- 43 -
Purge Data
200
After startup of filling line following settling of suspension.
Potency (mg/mL)
180
160
140
120
NIR
HPLC
100
80
0
100
200
300
400
500
600
700
800
Vial
- 44 -
Nyquist-Shannon Sampling Theorem
The sampling rate must be twice the
maximum frequency component of
the "signal" being measured,
otherwise aliasing will occur.
fsampling = 2 fsignal
Graphical representations see
Aliasing. Bruno A. Olshausen, PCS 129 – Sensory Processes, Oct 10, 2000.
http://redwood.ucdavis.edu/bruno/npb261/aliasing.pdf
- 45 -
Purge Data
(Short Timescale)
200
Potency (mg/mL)
180
160
140
120
NIR
HPLC
100
80
0
20
40
60
80
100
120
140
160
Vial
- 46 -
USP Compendial CU Testing
<905> “Uniformity of Dosage Units” in USP-NF
 Stage 1 Acceptance Criteria
Assay 10 samples, i.e., n = 10
Pass if RSD ≤ 6.0% and no value is outside 85% to 115% claim.
Fail if one or more value is outside 75% to 125% claim.
 Stage 2 Acceptance Criteria
Assay 20 more samples, i.e., n = 30
Pass if RSD ≤ 7.8%, no more than one value is outside 85% to
115% claim, and no value is outside 75% to 125% claim.
 Statistics are based on a small sample population;
i.e., analytical testing with low statistical power.
- 47 -
CU Testing Criteria for Large N
 USP <905> is unsuitable for data sets comprised of
large sample populations.
 Proposed Acceptance Criteria outlined in article:
Sandell D., Vukovinsky K., Diener M., Hofer J.,
Pazdan J., and Timmermans J. Development of a
Content Uniformity Test Suitable for Large
Sample Size. Drug Information Journal, Vol 40, pp.
337-344, 2006.
- 48 -
Example #2 – DP Dissolution Monitoring
 Objective
Provide a non-qualitative means of assessing completion of API
dissolution during compounding prior to aseptic filtration.
 Quality Impact Assessment
Indirect Impact.
Current IPC is by monitoring pH.
 Key Players
Justine McKenzie
Bob Witteman
Tim Wang
Bob Leasure
Project Management
Greenbelt, Manufacturing Engineer
Kalamazoo Injectable Manufacturing
Site PAT Support
- 49 -
Solu-Cortef Dissolution Monitoring
 Solu-Cortef is a sterile lyophilized parenteral product.
The hydrocortisone API is converted to the hemisuccinate sodium
salt by addition of base, with care not to exceed the specification
of pH 7.8.
O
O
O
O
O
HO
O
OH
O- Na
O
aqueous
HO
+Na
-O
+
O
OH
O
O
NaOH
+
O- Na
O
NaOH
HO
aqueous
Excess Base
O
O
O
 RDWitteman conducted a RFT Greenbelt study,
which concluded that slow response of the on-line pH probe can
lead to OOS final pH.
 On-line turbidity provides a more sensitive IPC over pH.
- 50 -
Solu-Cortef Dissolution Monitoring
- 51 -
Optek Forward Scatter Turbidity Probe
Optek Model AS16-N
Single Channel Photometer
•
Forward scatter Turbidity Probe
•
Operates in NIR from 730 to 970 nm
•
OPL from 1 to 40 mm
•
Aseptic Ingold or Triclover fittings
•
Analog controller, 4-20 mA I/O (no computer)
•
ca. $10K
- 52 -
Implementation Plans
 Optek Turbidity Probes have been installed in two CIP
compounding tanks in Kalamazoo’s new aseptic
production facility.
 C&Q of the analyzers is underway as part of the
validation of the new production facilities.
 Current plans are for the equipment to be used for
indirect impact process monitoring.
 Use of the equipment for direct impact process control
will be evaluated after additional process knowledge is
gained and with consideration of benefits from RFT and
Lean manufacturing.
- 53 -
Example #3 – API Solvent Recovery
 Application:
Cost Savings by Improving Yield for
Solvent Recovery in API Operations
 Quality Impact:
Direct Impact
 Issues:
Relatively slow determination of cut
for collecting product fraction.
Based on In-Plant Lab GC analysis.
 Project:
Install On-line Gas Chromatographic
analysis with associated automation.
(as deemed by QO)
- 54 -
OLGC Installation
 One of seven solvent recovery
columns at the site.
 Column #5 is used to recover
seven different solvents.

•
DMF
•
Methylene Chloride
•
Ethyl Acetate
•
THF
•
DMAP (THF containing alcohols)
•
Toluene
•
Acetone
Photo shows
•
Column
•
Still Pot
•
In-Plant Lab
- 55 -
Existing At-line GC Assay
 Performed by manufacturing operators in the
“In-Plant Lab” (IPL)
 Analysis is time consuming due to manual steps:
•
Collect sample
•
Transport to IPL
•
Sample preparation and injection
•
Assay runtime,
as long as 45 minutes depending on solvent
 Prompt for manual analysis is based on column
temperatures and “wait” times indicated in Master Record
- 56 -
Siemens Maxum II On-line GC
Dual Oven, Isothermal GC
Calibration
Standard
Sampling Valves
- 57 -
On-line GC Schematic
Column 2 Forward
Column 1 Forward (main) Column 1 Reverse
(ITC)
(BF main)
Detector Vents
S
S
S
R
Restrictors
2
1
SSO
3
10
4
9
5
8
6
Column 1
Carrier In
from EPC
Column 2
7
Sample Sample
Out
In
- 58 -
Automation
Backup of data files and
configuration from
WKS1 on AMER
domain resource.
Network Fileshare Storage
PDH OPC Client
Runs Workstation
and OPC Server/
Client software
interfaced to
B362S927.
Member of AMER
domain.
Gets PAT data either
from APP node or
directly from WKS1
PCN Switch in
B362
B362OPC001
pe362hb
WKS1 (B362)
Controlled by Workstation
software on WKS1
Runs OPC Server/
Client Interface to
WKS1. Member of
AMER domain.
APP Node (B362S927)
GC Instrument (B73)
DCS (073HWL04)
In-Plant
Lab System
- 59 -
Right Oven FID (High Boiling Organics)
- 60 -
Right Oven TCD
(Water)
- 61 -
Left Oven FID
(Low Boiling Organics)
- 62 -
Method Validation
 Method parameters assessed during the validation using a
black-box approach, but still addressing the following:
●
Specificity
○
Accuracy
●
Precision – Repeatability
○
Detection Limit
●
Linearity
○
Range
●
Quantitation Limit
Component
Type
Major
Minor
Constituent
acetone
water
methylene chloride
ethyl acetate
tetrahydrofuran
toluene
methanol
ethanol
*
†
‡
Limits*
(vol %)
NLT 98.5
none
none
none
none
none
NMT 0.5
none
Linearity
Range†
(vol %)
0 to
0 to
0 to
0 to
0 to
0 to
0 to
5
5
2
2
2
2
2
Working
Range‡
(vol %)
0
0
0
0
0
0
0
to
to
to
to
to
to
to
30
20
1
1
0.5
1
0.5
Siemens
Repeatability
Specification‡
± 3%
± 0.5%
± 1%
± 1%
± 1%
± 1%
± 1%
Repeatability
(vol %)
± 0.9
± 0.1
± 0.01
± 0.01
± 0.005
± 0.01
± 0.005
NLT is not less than. NMT is not more than.
The "Linearity Range" may differ from the "Working Range" and spans the region where linearity criteria are applied.
Repeatability is based on Siemens specification for 8 hour repeatibility, expressed as a percentage of "Working Range".
- 63 -
Sample Preparation
Each sample solution prepared according to the following instructions.
1. Half-fill the indicated size volumetric flask with the major component solvent.
2. Add spike volumes of each indicated neat minor component or stock solution to the flask by using Class A volumetric pipettes.
For volumes greater than 20 mL, a graduated cylinder may be used to measure the volume of the minor component being added.
If a stock solution is used, then only one addition of the stock is needed to meet the spike levels for minor components.
3. q.s. with the major component solvent; i.e., acetone.
Stock Solutions
Sample or Solution ID
Major Component: acetone
Limit: NLT 98.5 vol%
Volume required for preps: 4100 mL
Volumetric Flask Size (mL)
Spike Solution
vol %
vol %
vol %
Minor Component: methylene chloride
Limit: NMT 0.2 vol %
Linearity Range: 0 to 5
vol %
Working Range: 0 to 20
vol %
Minor Component: ethyl acetate
Limit: NMT 0.3 vol %
Linearity Range: 0 to 2
vol %
Working Range: 0 to 1
vol %
Stock #2
blank
1
2
3
4
5
6
7
50
500
500
500
500
500
500
500
500
neat
neat
neat
Stock #1
Stock #2
neat
neat
neat
neat
neat
10
10
Spike Volume (mL)
3
8
n/a
n/a
8
3
20
50
150
Target Level (vol %)
% of Linearity Range
% of Working Range
Minor Component Percentage
6
16
0.120
2.4%
0.4%
15.0%
0.320
6.4%
1.1%
36.4%
1.6
32.0%
5.3%
22.2%
0.6
12.0%
2.0%
9.7%
4
80.0%
13.3%
30.3%
10
200.0%
33.3%
33.3%
30
600.0%
100.0%
75.0%
2
7
20
100
50
0.040
0.8%
0.2%
5.0%
0.160
3.2%
0.8%
18.2%
0.4
8.0%
2.0%
5.6%
1.4
28.0%
7.0%
22.6%
4
80.0%
20.0%
30.3%
20
400.0%
100.0%
66.7%
10
200.0%
50.0%
25.0%
10
2
5
0
0
0.040
2.0%
4.0%
5.0%
0.120
6.0%
12.0%
13.6%
2
100.0%
200.0%
27.8%
0.4
20.0%
40.0%
6.5%
1
50.0%
100.0%
7.6%
0
0.0%
0.0%
0.0%
0
0.0%
0.0%
0.0%
0.320
16.0%
32.0%
40.0%
0.120
6.0%
12.0%
13.6%
2
0.4
20.0%
40.0%
5.6%
5
1
50.0%
100.0%
16.1%
10
2
100.0%
200.0%
15.2%
0
0
0.0%
0.0%
0.0%
0
0
0.0%
0.0%
0.0%
0.040
4.0%
8.0%
5.0%
0.080
8.0%
16.0%
9.1%
1
0.2
20.0%
40.0%
2.8%
3
0.6
60.0%
120.0%
9.7%
6
1.2
120.0%
240.0%
9.1%
0
0
0.0%
0.0%
0.0%
0
0
0.0%
0.0%
0.0%
0.160
8.0%
16.0%
20.0%
0.040
2.0%
4.0%
4.5%
7
1.4
70.0%
140.0%
19.4%
10
2
100.0%
200.0%
32.3%
2
0.4
20.0%
40.0%
3.0%
0
0
0.0%
0.0%
0.0%
0
0
0.0%
0.0%
0.0%
0.080
4.0%
16.0%
10.0%
0.040
2.0%
8.0%
4.5%
6
1.2
60.0%
240.0%
16.7%
1
0.2
10.0%
40.0%
3.2%
3
0.6
30.0%
120.0%
4.5%
0
0
0.0%
0.0%
0.0%
0
0
0.0%
0.0%
0.0%
Spike Volume (mL)
1
4
Target Level (vol %)
% of Linearity Range
% of Working Range
Minor Component Percentage
2
8
Spike Volume (mL)
1
3
2
6
Minor Component: tetrahydrofuran
Limit: NMT 0.5 vol %
Linearity Range: 0 to 2
vol %
Working Range: 0 to 1
vol %
Spike Volume (mL)
Target Level (vol %)
% of Linearity Range
% of Working Range
Minor Component Percentage
8
16
3
6
Minor Component: toluene
Limit: NMT 0.1
Linearity Range: 0 to 1
Working Range: 0 to 0.5
Spike Volume (mL)
Target Level (vol %)
% of Linearity Range
% of Working Range
Minor Component Percentage
1
2
2
4
Minor Component: methanol
Limit: NMT 0.5 vol %
Linearity Range: 0 to 2
vol %
Working Range: 0 to 1
vol %
Spike Volume (mL)
Target Level (vol %)
% of Linearity Range
% of Working Range
Minor Component Percentage
4
8
1
2
Minor Component: ethanol
Limit: NMT 0.1
Linearity Range: 0 to 2
Working Range: 0 to 0.5
Spike Volume (mL)
Target Level (vol %)
% of Linearity Range
% of Working Range
Minor Component Percentage
2
4
1
2
vol %
vol %
vol %
preps from neat minor components
50
Target Level (vol %)
% of Linearity Range
% of Working Range
Minor Component Percentage
vol %
vol %
vol %
preps from stock
Stock #1
Stock Spike Volume (mL)
Minor Component: water
Limit: NMT 0.5
Linearity Range: 0 to 5
Working Range: 0 to 30
blank
- 64 -
an
ol
m
1
n
w
at
e
ac
et
et
at
hy
e
le
ne
ch
lo
rid
e
et
hy
l
of
ur
a
to
lu
en
e
et
h
tra
hy
dr
te
m
ol
an
k
et
ha
n
bl
e
pl
m
2
Sa
r
3
4
0
5
6
7
1
2
Volume %
3
4
5
Sample Preparation
ent
mpon
o
C
r
Mino
- 65 -
Analyte Ratios – Assessment of Specificity
100%
90%
Relative Percent of Minor Component
80%
70%
ethanol
methanol
toluene
tetrahydrofuran
60%
50%
ethyl acetate
methylene chloride
40%
water
30%
20%
10%
0%
1
2
3
4
5
6
7
Sample #
- 66 -
Sample ID
volume %
Regression Analysis
Linest Statistics
0.973113975 0.007696776
0.007619648 0.007140433
0.999693536 0.014973114
16310.13646
5
3.656637021 0.001120971
"X" Range
0
2
"Y" Fit Value
0.008
1.954
Measured (vol %)
0.000
0.219
0.176
blank
Linear Regression
0.086
intercept:
0.00770
Component:
0.073 methanol
slope:
0.97311
residual
sum
of
squares:
0.00112
Limit:
0.000 0.5
correlation coefficient:
0.99910
Repeatability Specification:
square of correlation coefficient:
0.99819
0.177 0.01
std error for the y-estimate of the regression line:
0.01497
0.178 0 to 2
Linearity Range:
limit of detection:
0.05078
limit
of
quantitation:
0.15387
0.177
1
0.174
Measured
2.5
0.174
Sample
Theoretical
Median
Average
Std Dev
Recovery
Repeatability
Median
blank
0 0.175
0.080
0.092
0.090
Average
2.0
Fit
1
0.160.059
0.176
0.176
0.002
110%
0.002
Pass
0.060
2
0.04
0.060
0.060
0.001
149%
0.001
Pass
1.5
0.059
2
3
1.40.060
1.389
1.384
0.009
99%
0.009
Pass
4
2 0.060
1.948
1.948
0.014
97%
0.014
Pass
1.0
5
0.40.059
0.374
0.374
0.011
94%
0.011
Pass
6
0 1.389
0.000
0.005
0.008
0.008
Pass
0.5
7
0 1.389
0.000
0.000
0.000
0.000
Pass
1.389
Average:
110%
0.006
3
0.0
1.380 used for assessing validation criteria.
Data in blue boxes
0.0
0.5
1.0
1.5
1.389
Theoretical (vol %)
1.366
1.950
1.946
Criterion
1
The slope of the linearity plot of measured volume % vs. theoretical volume % for each minor component of
1.945
4
1.950
interest must be 1 ± 0.2.
1.969
Result:
Pass
1.926
0.394
0.377
Criterion
2
For each minor component of interest, the repeatability for each solution (for which six consecutive repeat
0.372
5
0.367
injections were made) must be equal to or less than the respective repeatability specification provided in
0.361
Table 1.
0.375
Result:
Pass
0.019
0.013
0.000
6
Criterion
3
For each minor component of interest, the square of the regression coefficient from the plot of measured
0.000
0.000
volume % vs. theoretical volume % must be 0.99 or better.
0.000
Result:
Pass
0.000
0.000
0.000
7
Criterion
4
The QL must be less than 50% the limit for the respective minor component.
0.000
0.000
Result:
Pass
0.000
2.0
2.5
- 67 -
Issue: Frequent Failure of Injection Rotor
 The variety of solvent polarity and incompatibility of MOC caused
“grooving” of the injector rotor
 Fix involved specifying a different PTFE coated rotor.
- 68 -
Projected Savings
The Return on Investment of the implementation was estimated to be one year,
based on solvent cost and production volumes at the time of CPA submittal.
Price per Gallon
Approximate
% of ROI
Toluene
$ 2.54
59%
Ethyl acetate
$ 3.44
20%
Tetrahydrofuran
$ 8.59
8%
Dimethylformamide
$ 3.90
7%
Methylene Chloride
$ 3.67
3%
THF
$ 8.59
2%
$ 3.07
1%
Solvent
(alcohol containing stream)
Acetone
- 69 -
Lessons Learned
 Stick to the Plan
Do not deviate from define validation approach
established at the beginning of the project;
otherwise the project may be delayed.
 Train Appropriate Personnel Appropriately
Cross-train key users for daily care and
troubleshooting of the instrument.
User training should be budgeted as part of the
project scope.
 Keep it Simple
Depending on the technology, analysis of multiple
streams/products may present challenges and
additional overhead.
- 70 -
Acknowledgements
 Drug Product Suspension CU
•
Tom Garner
- RFT Black Belt and Project Manager
 Drug Product Dissolution Monitoring
•
Robert Wittemann
- RFT Green Belt and Production Engineer
•
Tim Wang
- PPT Production Engineering
 On-line GC for Solvent Recovery
•
Brad Diehl
- PASG Implementation Support
•
Frank Sistare
- PGM Groton
•
Joe Geiger
- Production Engineering Solvent Recovery
•
Jeff Terpstra
- Project Management
•
Pete Miilu, Marc Surprenant - IT Automation
•
Donald Zeilenga
•
Scott Wagenaar, Kurt Holton - Production Operations
•
Andrew Meister
- KPT and Site PAT Support
- Instrumentation Maintenance
- 71 -
- 72 -