- 1 - A Roadmap for PAT Implementation in Pharmaceutical Manufacturing Robert M. Leasure Principal Scientist Site PAT Champion Pfizer Global Manufacturing.
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- 1 -
A Roadmap forPAT Implementation in
Pharmaceutical Manufacturing
Robert M. LeasurePrincipal ScientistSite PAT Champion
Pfizer Global Manufacturing7000 Portage Road, PORT-91-201Kalamazoo, MI 49001(269) 833-6198
- 2 -
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 ProductDissolution Monitoring of Active during pH adjustment
• API OperationsSolvent Recovery – improved yield from timely fraction determination.
- 3 -
Definitions
What is PAT?Process Analytical Technologies
Probes in TanksAnalyzers in Plant
Automation
Process Data (lots of it)
Things 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?
Questions that come to mind…..
and Questions
- 4 -
The answer is multivariate and transient.
It depends on who is asking the question,and who is giving the answer.
Technologists
Managers $$$
Quality andRegulatory Groups
Support Groups
IT,Engineering, Maintenance
What is PAT?
- 5 -
Spectrometer
Automation
Reactor
Control Room
Probe
AnalyticalInstrument
Automation
Reactor
Control Room
Probe
AnalyticalInstrument
Automation
Fiber-Optic Probe
FeedbackControl
Bona fideOn-line
PAT SystemNear-Infrared
Analog RecorderpH Probe
At-line
Reactor In-Plant Laboratory
SampleValve
Pfizer
Reactor In-Plant Laboratory
SampleValve
Pfizer
On-line
vs.
Off-line
What is PAT?(a)
- 6 -
FDA Guidance on PAT
Ajaz S. Hussain, Ph.D.Previously Deputy Directory Office ofPharmaceutical Science, CDER, FDA
FDA Guidance Document on PATReleased in September 2004.
http://www.fda.gov/cder/guidance/6419fnl.htm
Key proponent for the use of PAT inthe pharmaceutical industry.
- 7 -
FDA Definition of PAT
FDA Guidance – September 2004PAT – A Framework for Innovative Pharmaceutical Manufacturing and Quality Assurance
Line 158:“For the purposes of this guidance document, PAT is considered to be a system for designing, analyzing, and controlling manufacturing through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and in-process materials and processes with the goal of ensuring final product quality.”
Line 158:“For the purposes of this guidance document, PAT is considered to be a system for designing, analyzing, and controlling manufacturing through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and in-process materials and processes with the goal of ensuring final product quality.”
- 8 -
Who benefits from (a) PAT?
The Users
Technologists
Managers $$$
Quality andRegulatory Groups
Support Groups
IT,Engineering, Maintenance
What is (a) PAT?
1. Manufacturing Operations
2. R&D or Process Scientists
- 9 -
Where does PAT begin (and end)?Co-development or
Continuous Improvement Activities
ManufacturingOperations
PAT Project Progression
Invo
lve
men
t
"Early PAT" Used to determine
Critical Process Parameters
"Late PAT" Used to control the process
Requires formal validation Low cost / benefit ratio
R&D or
Process Support * Proceed with PATs
in development?
- 11 -
Improved quality. Improved safety.
Cost savings.
Why do PAT?
Process Control
Process Knowledge
Improved quality. Improved safety.
Cost savings.
Improved quality. Improved safety.
Cost savings.Fundamental Goals
Well Controlled Process
RFTRFT
- 12 -
Continuous Quality Verification
Process
What is done on the plant floor.
Inputs Metrics
Dat
a
Evaluation
Requirements
Root CauseAnalysisAction
PeopleEquipmentProceduresMaterials
Cost
Schedule
Quality (Compliance)
Well Controlled Process Model
Process AnalyticsProcess Analytics
- 13 -
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.
- 14 -
Six Questions
What do you want to measure?
How do you want to measure it?
Where do you want to measure?
When do you want to measure it?
Why do you want to measure it?
Who will look at the results??
?
?
?
?
?
Chemical or physical property.
Analytical technology.
Process Knowledge or Process Control?
Before, during, or after a process step?
Sampling frequency.
Validation…..
- 15 -
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 probematerial of construction compatibility?
- 16 -
PAT System Qualification
PAT System Qualification and Method Validationshould be based on intended use of data.
Three Levels
1. Development or Proof of Concept
2. Information Only
3. Release Decisions
Quality Impact
No Impact
Indirect Impact
Direct Impact
Validation or Commissioning and Qualificationmust conform to applicable:
Corporate Quality Standards
Site Procedures
- 17 -
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
- 18 -
PAT Development Resources for Kalamazoo
Active Pharmaceutical Ingredients Drug Product
• Sterile Injectables• Non-sterile Fluids and Ointments
• Fermentation Operations• Chemical Operations
Two main manufacturing operations:
Site Technology Groups
Site PAT Group
Process AnalyticalSupport Group (PASG)
Right First Time (Black, Green, Yellow
Belts)
Center Function Support
Kalamazoo ProcessTechnology (KPT)
Product and ProcessTechnology (PPT)
- 20 -
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
- 21 -
Project PrioritizationWeighting Factor:
2 2 3 3 1 1 1 3
Project
Bu
sin
ess
Are
a
Incr
ease
d P
roce
ss
Und
erst
andi
ng
Qua
lity
Impr
ovem
ent
Impr
oved
Eff
icie
ncy
or
Pro
cess
Im
prov
emen
t
EH
S Im
prov
emen
t
Pro
ject
Com
plex
ity
(dif
ficu
lt =
1, s
imp
le =
10)
Impl
emen
tato
in C
ost
(>$3
00K
= 1
, <$1
0K =
10)
Reg
ulat
ory
Con
stra
ints
(h
igh
er is
less
con
stra
ign
ed)
Site
Spe
cifi
c C
rite
rion
Ran
k as
a P
erce
ntag
e
Raman ID of Incoming Raw Materials QO 8 6 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%
Turbidity Dissolution Endpoint 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%
OLMS Ceplasporin Dryer Monitoring API 3 4 8 10 5 3 5 5 60%
- 22 -
Technology Development Process
PAT Project Ideas
Justificationreview andprojectprioritization
Lab proof ofconcept
Project specific teamorganized
Plant proof ofconcept
Decision toproceed
Adapted from an illustration by Seamus O’Neill (PASG, Ireland)
ProductionQuality OperationsEHSTechnology GroupsAutomationEngineeringPAT Champion
PAT ChampionPASGTech GroupsVendor
PAT ChampionProductionQuality OpsEHSTech GroupsAutomationEngineering
Project TeamPASGVendor
Project TeamSite ManagementPASG
SIMP
Site Imple-mentation Plan
Development
CPA
(if needed)
Plant POC Report
Tech Report on Lab POC Studies
PAT Project Charter
- 23 -
PAT ImplementationTeam
PATProject
PATChampion
RFTChampion
ManufacturingOperations
R&D(co-dev)
QualityOperations
Engineering
Regulatory
Environmental,Health and Safety
ValidationServices
Automation
InformationTechnology
Management
Maintenance
Tech Services(KPT or PPT)
PASG
Implementation of a PAT requires input from a multi-disciplinary team.
- 24 -
GAMP Model for Instrument Qualification
User Requirements
Functional Specifications
Design Specifications
Installation
Installation Qualification
Operational Qualification
Performance Qualification
Good Automated Manufacturing PracticeGood Automated Manufacturing Practice
- 25 -
More Questions
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)
What are you going to do with the data?
Really asking:
Is the PAT for Process Knowledge or Process Control ?
Answer: Quality Impact Assessment document
- 26 -
URS
User RequirementsSpecifications
QIA
QualityImpactAssessment
Ready forRoutine
Operation?
Implementation Process
DefineRequirements
PAT TeamPASG
VendorProject TeamPASGValidation Services
ProductionQualityPAT Champion
Cost review, justification, vendor selection,and approval
FAT, SAT, installation,qualification
Applicationverification
DefinitiveCPA
Capital ProjectApproval
FDS
FunctionalDesignSpecifications
IQ/OQ
Installation and Operation Qualification
PQ
Performance Qualification
Lifecycle Docs• Analytical Methods• Operation SOPs• Maintenance SOPs• Training Docs• Change Control• Periodic Review•Business Continuity Plan
Cross SiteLearning
Project TeamPASGVendor
Adapted from an illustration by Seamus O’Neill (PASG, Ireland)
RoutineOperation
- 27 -
Example #1 – CU in a Sterile Suspension
Application: Drug ProductSterile Aqueous Suspension
Quality Impact: No Impact, Process Knowledge(product was not for sale)
Objective: Improved Content Uniformityduring later stages of filling
operation.
Project: RFT and Continuous Improvement
Black Belt project to providesuggested process changes forimproved content uniformity.
- 28 -
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
- 29 -
Sterile Suspension Filling Operation
On-line Turbidity of Bulk Suspension Recycle Loop
Off-line or At-LineNIR Analysisof Filled Vials
- 30 -
Potency vs. Amount Filled
Filling operation is controlled within specifications, but thereis opportunity for improvement near the end of the batch.
Lot A
140
145
150
155
160
165
0 20 40 60 80 100
Approximate Percent Filled
Pot
ency
(m
g/m
L)
Off-line NIR HPLC
RSDNIR = 0.44%
RSDHPLC = 0.83%
RSDNIR = 1.91%
RSDHPLC = 2.57%
Lot B
140
145
150
155
160
165
170
0 20 40 60 80 100
Approximate Percent Filled
Pot
ency
(m
g/m
L)
Off-line NIR HPLC
RSDNIR = 0.55%
RSDHPLC = 0.49%
RSDNIR = 3.04%
RSDHPLC = 4.47%
- 31 -
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
- 32 -
Sample Spinner Schematic
45 °
sample
fiber optic probe
needle bearing
sleeve holder
mountingbracket
rotating gear( = 125 rpm)
- 33 -
Effect of Spin-rateon Apparent Concentration
130
150
170
190
210
230
250
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
Time (min)
App
aren
t Con
cent
rati
on (
mg/
mL
) 0 rpm
25 rpm
50 rpm
125 rpm
- 34 -
Raw Near-IR Spectra
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1100 1300 1500 1700 1900 2100
Wavelength (nm)
Abs
orba
nce
log
(1/R
)
187
168
150
131
114
100
125
150
175
200
Sample
Pot
ency
(m
g/m
L)
- 35 -
1st Derivative Spectra
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
1100 1300 1500 1700 1900 2100
Wavelength (nm)
1st D
eriv
ativ
e
0.00
0.04
0.08
0.12
1250 1300 1350 1400 1450 1500
-0.010
-0.005
0.000
0.005
0.010
0.015
1600 1650 1700 1750 1800 1850
- 36 -
Near-IR Calibration
100
110
120
130
140
150
160
170
180
190
200
100 110 120 130 140 150 160 170 180 190 200
Lab Potency (mg/mL)
NIR
Pot
ency
(m
g/m
L)
Training Set (SEC = 1.28 mg/mL, R = 0.99)Test Set (SEP = 1.40 mg/mL, R = 0.99)
Partial Least Squares Model2 factors, 1st derivative, 1650-1800 nm
- 37 -
Optek Turbidity Sensor
1. Sensor Body2. Windows3. NIR Filter4. Photo Diode5. Optics Module6. Tungsten Lamp
- 38 -
Calibration of On-line Turbidity Sensor
Incrementally dilute a concentrated suspension with known amounts of vehicle.
Correlate calculated suspension potency with turbidity sensor response.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0 10 20 30 40 50Time (min)
A/D
Con
vert
er (
volts
)
117
122
127
132
137
142
147
152
157
162
Pot
ency
(m
g/m
L)
158.3 mg/mL
153.7
148.5
140.9
135
140
145
150
155
160
165
1.5 2.0 2.5 3.0 3.5
Optek Signal (volts)
Cal
cula
ted
Pot
ency
(m
g/m
L)
Calcuated Potency = 116.6 + 12.95 (OpSig) R² = 0.995
- 39 -
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
- 40 -
DOE Results
Bottom mixer has minimal contribution to mixing.
- 41 -
DOE Results continued Mixer power is critical for consistent CU.
- 42 -
Improved Filling Process Proposed process change: leave mixer on longer.
Three lots demonstrated no dip and no tail at end of fill.
- 43 -
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.
- 44 -
Purge Data
80
100
120
140
160
180
200
0 100 200 300 400 500 600 700 800
Vial
Pot
ency
(m
g/m
L)
NIR
HPLC
After startup of filling line following settling of suspension.
- 45 -
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 seeAliasing. Bruno A. Olshausen, PCS 129 – Sensory Processes, Oct 10, 2000.http://redwood.ucdavis.edu/bruno/npb261/aliasing.pdf
- 46 -
Purge Data (Short Timescale)
80
100
120
140
160
180
200
0 20 40 60 80 100 120 140 160
Vial
Pot
ency
(m
g/m
L)
NIR
HPLC
- 47 -
USP Compendial CU Testing
<905> “Uniformity of Dosage Units” in USP-NF
Stage 1 Acceptance CriteriaAssay 10 samples, i.e., n = 10Pass 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 CriteriaAssay 20 more samples, i.e., n = 30Pass 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.
- 48 -
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.
- 49 -
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 PlayersJustine McKenzie Project ManagementBob Witteman Greenbelt, Manufacturing EngineerTim Wang Kalamazoo Injectable ManufacturingBob Leasure Site PAT Support
Example #2 – DP Dissolution Monitoring
- 50 -
Solu-Cortef Dissolution Monitoring
O
OH
O
O
OOH
O
O
OH
O
O
O
O
O- Na+
O
OH
OH
O
Na+O
O
O-
-O
NaOH
aqueous
+Na
NaOH
aqueous
Excess Base
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.
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.
- 51 -
Solu-Cortef Dissolution Monitoring
- 52 -
Optek Forward Scatter Turbidity Probe
Optek Model AS16-NSingle 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
- 53 -
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.
- 54 -
Example #3 – API Solvent Recovery
Application: Cost Savings by Improving Yield forSolvent Recovery in API Operations
Quality Impact: Direct Impact (as deemed by QO)
Issues: Relatively slow determination of cutfor collecting product fraction.Based on In-Plant Lab GC analysis.
Project: Install On-line Gas Chromatographicanalysis with associated automation.
- 55 -
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
- 56 -
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
- 57 -
Siemens Maxum II On-line GC
Dual Oven, Isothermal GC
Sampling ValvesCalibrationStandard
- 58 -
On-line GC Schematic
Column 1 Forward(ITC)
Column 2 Forward(main) Column 1 Reverse
(BF main)
S S S R
Column 1
Column 2
12
10
9
8
3
4
5
76
Carrier Infrom EPC
SampleOut
SampleIn
SSO
Detector Vents
Restrictors
- 59 -
Automation
In-PlantLab System
DCS (073HWL04)
WKS1 (B362)
GC Instrument (B73)
pe362hb
APP Node (B362S927)
B362OPC001
PDH OPC Client
Runs OPC Server/Client Interface to WKS1. Member of
AMER domain.
Controlled by Workstation software on WKS1
Runs Workstation and OPC Server/Client software interfaced to B362S927.
Member of AMER domain.
Network Fileshare Storage
Backup of data files and configuration from WKS1 on AMER domain resource.
Gets PAT data either from APP node or
directly from WKS1
PCN Switch in B362
- 60 -
Right Oven FID (High Boiling Organics)
- 61 -
Right Oven TCD (Water)
- 62 -
Left Oven FID (Low Boiling Organics)
- 63 -
Method Validation
● Specificity
● Precision – Repeatability
● Linearity
● Quantitation Limit
Method parameters assessed during the validation using ablack-box approach, but still addressing the following:
○ Accuracy
○ Detection Limit
○ Range
ComponentType
Constituent
Major acetone NLT 98.5
water none 0 to 5 0 to 30 ± 3% ± 0.9
methylene chloride none 0 to 5 0 to 20 ± 0.5% ± 0.1
ethyl acetate none 0 to 2 0 to 1 ± 1% ± 0.01
tetrahydrofuran none 0 to 2 0 to 1 ± 1% ± 0.01
toluene none 0 to 2 0 to 0.5 ± 1% ± 0.005
methanol NMT 0.5 0 to 2 0 to 1 ± 1% ± 0.01
ethanol none 0 to 2 0 to 0.5 ± 1% ± 0.005
Linearity
Range†
(vol %)
Working
Range‡
(vol %)
Minor
* 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".
Repeatability(vol %)
Limits*
(vol %)
SiemensRepeatability
Specification‡
- 64 -
Sample PreparationEach 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.
blank
Major Component: Sample or Solution ID Stock #1 Stock #2 blank 1 2 3 4 5 6 7
Limit: 98.5 vol% Volumetric Flask Size (mL) 50 50 500 500 500 500 500 500 500 500
4100 mL Spike Solution neat neat neat Stock #1 Stock #2 neat neat neat neat neat
Stock Spike Volume (mL) 10 10
Minor Component: Spike Volume (mL) 3 8 n/a n/a 8 3 20 50 150
Limit: 0.5 vol % Target Level (vol %) 6 16 0.120 0.320 1.6 0.6 4 10 30Linearity Range: 0 to 5 vol % % of Linearity Range 2.4% 6.4% 32.0% 12.0% 80.0% 200.0% 600.0%Working Range: 0 to 30 vol % % of Working Range 0.4% 1.1% 5.3% 2.0% 13.3% 33.3% 100.0%
Minor Component Percentage 15.0% 36.4% 22.2% 9.7% 30.3% 33.3% 75.0%
Minor Component: Spike Volume (mL) 1 4 2 7 20 100 50
Limit: 0.2 vol % Target Level (vol %) 2 8 0.040 0.160 0.4 1.4 4 20 10Linearity Range: 0 to 5 vol % % of Linearity Range 0.8% 3.2% 8.0% 28.0% 80.0% 400.0% 200.0%Working Range: 0 to 20 vol % % of Working Range 0.2% 0.8% 2.0% 7.0% 20.0% 100.0% 50.0%
Minor Component Percentage 5.0% 18.2% 5.6% 22.6% 30.3% 66.7% 25.0%
Minor Component: Spike Volume (mL) 1 3 10 2 5 0 0
Limit: 0.3 vol % Target Level (vol %) 2 6 0.040 0.120 2 0.4 1 0 0Linearity Range: 0 to 2 vol % % of Linearity Range 2.0% 6.0% 100.0% 20.0% 50.0% 0.0% 0.0%Working Range: 0 to 1 vol % % of Working Range 4.0% 12.0% 200.0% 40.0% 100.0% 0.0% 0.0%
Minor Component Percentage 5.0% 13.6% 27.8% 6.5% 7.6% 0.0% 0.0%
Minor Component: Spike Volume (mL) 8 3 2 5 10 0 0
Limit: 0.5 vol % Target Level (vol %) 16 6 0.320 0.120 0.4 1 2 0 0Linearity Range: 0 to 2 vol % % of Linearity Range 16.0% 6.0% 20.0% 50.0% 100.0% 0.0% 0.0%Working Range: 0 to 1 vol % % of Working Range 32.0% 12.0% 40.0% 100.0% 200.0% 0.0% 0.0%
Minor Component Percentage 40.0% 13.6% 5.6% 16.1% 15.2% 0.0% 0.0%
Minor Component: Spike Volume (mL) 1 2 1 3 6 0 0
Limit: 0.1 vol % Target Level (vol %) 2 4 0.040 0.080 0.2 0.6 1.2 0 0Linearity Range: 0 to 1 vol % % of Linearity Range 4.0% 8.0% 20.0% 60.0% 120.0% 0.0% 0.0%Working Range: 0 to 0.5 vol % % of Working Range 8.0% 16.0% 40.0% 120.0% 240.0% 0.0% 0.0%
Minor Component Percentage 5.0% 9.1% 2.8% 9.7% 9.1% 0.0% 0.0%
Minor Component: Spike Volume (mL) 4 1 7 10 2 0 0
Limit: 0.5 vol % Target Level (vol %) 8 2 0.160 0.040 1.4 2 0.4 0 0Linearity Range: 0 to 2 vol % % of Linearity Range 8.0% 2.0% 70.0% 100.0% 20.0% 0.0% 0.0%Working Range: 0 to 1 vol % % of Working Range 16.0% 4.0% 140.0% 200.0% 40.0% 0.0% 0.0%
Minor Component Percentage 20.0% 4.5% 19.4% 32.3% 3.0% 0.0% 0.0%
Minor Component: Spike Volume (mL) 2 1 6 1 3 0 0
Limit: 0.1 vol % Target Level (vol %) 4 2 0.080 0.040 1.2 0.2 0.6 0 0Linearity Range: 0 to 2 vol % % of Linearity Range 4.0% 2.0% 60.0% 10.0% 30.0% 0.0% 0.0%Working Range: 0 to 0.5 vol % % of Working Range 16.0% 8.0% 240.0% 40.0% 120.0% 0.0% 0.0%
Minor Component Percentage 10.0% 4.5% 16.7% 3.2% 4.5% 0.0% 0.0%
NMT
NMT
acetone
water
NLT
NMT
preps from neat minor components
Volume required for preps:
preps from stockStock Solutions
ethanolNMT
methylene chlorideNMT
ethyl acetateNMT
tetrahydrofuranNMT
toluene
methanol
- 65 -
Sample Preparation
blan
k
1
2
3
4
5
6
7
water
meth
ylene
chlor
ide
ethyl
aceta
te
tetrah
ydro
fura
n
tolue
ne
meth
anol
ethan
ol
01
23
45
Vol
ume
%
Sample
Minor Component
- 66 -
Analyte Ratios – Assessment of Specificity
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4 5 6 7
Sample #
Rel
ativ
e P
erce
nt
of M
inor
Com
pon
ent
ethanol
methanol
toluene
tetrahydrofuran
ethyl acetate
methylene chloride
water
- 67 -
Regression AnalysisComponent: methanol
Limit: 0.5Repeatability Specification: 0.01
Linearity Range: 0 to 2
Sample Theoretical Median Average Std Dev Recoveryblank 0 0.080 0.092 0.090
1 0.16 0.176 0.176 0.002 110% 0.002 Pass2 0.04 0.060 0.060 0.001 149% 0.001 Pass3 1.4 1.389 1.384 0.009 99% 0.009 Pass4 2 1.948 1.948 0.014 97% 0.014 Pass5 0.4 0.374 0.374 0.011 94% 0.011 Pass6 0 0.000 0.005 0.008 0.008 Pass7 0 0.000 0.000 0.000 0.000 Pass
Average: 110% 0.006Data in blue boxes used for assessing validation criteria.
MeasuredRepeatability
Criterion 1
Result: Pass
Criterion 2
Result: Pass
Criterion 3
Result: Pass
Criterion 4 The QL must be less than 50% the limit for the respective minor component.Result: Pass
For each minor component of interest, the square of the regression coefficient from the plot of measured volume % vs. theoretical volume % must be 0.99 or better.
The slope of the linearity plot of measured volume % vs. theoretical volume % for each minor component of interest must be 1 ± 0.2.
For each minor component of interest, the repeatability for each solution (for which six consecutive repeat injections were made) must be equal to or less than the respective repeatability specification provided in Table 1.
Linear Regression 0.973113975 0.007696776intercept: 0.00770 0.007619648 0.007140433
slope: 0.97311 0.999693536 0.014973114residual sum of squares: 0.00112 16310.13646 5
correlation coefficient: 0.99910 3.656637021 0.001120971square of correlation coefficient: 0.99819
std error for the y-estimate of the regression line: 0.01497 "X" Range "Y" Fit Valuelimit of detection: 0.05078 0 0.008
limit of quantitation: 0.15387 2 1.954
Linest Statistics
0.0
0.5
1.0
1.5
2.0
2.5
0.0 0.5 1.0 1.5 2.0 2.5
Theoretical (vol %)
Mea
sure
d (
vol %
)
Median
Average
Fit
0.0000.2190.1760.0860.0730.0000.1770.1780.1770.1740.1740.1750.0590.0600.0590.0600.0600.0591.3891.3891.3891.3801.3891.3661.9501.9461.9451.9501.9691.9260.3940.3770.3720.3670.3610.3750.0190.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Sample ID volume %
blank
5
6
7
1
2
3
4
- 68 -
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.
- 69 -
Projected Savings
Solvent Price per GallonApproximate
% 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(alcohol containing stream)
$ 8.59 2%
Acetone $ 3.07 1%
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.
- 70 -
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 AppropriatelyCross-train key users for daily care and troubleshooting of the instrument.User training should be budgeted as part of the project scope.
Keep it SimpleDepending on the technology, analysis of multiple streams/products may present challenges and additional overhead.
- 71 -
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 - KPT and Site PAT Support
• Scott Wagenaar, Kurt Holton - Production Operations
• Andrew Meister - Instrumentation Maintenance
- 72 -
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