Process Validation Presentation from BioTechLogic

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LOGIC
BioTechLogic, Inc.
BIO TECH
LOGICBIO ®
Stage 1:
Process Design
Stage 2: Process
Qualification
Stage 3: Continued
Process Verification
Explore Process
Validation
Click on a link below (Stage 1, 2, or 3) to
view a presentation on the various stages
of Process Validation and how
BioTechLogic can help you succeed in your
validation efforts.
Slide 1
Company Confidential

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LOGICBIO ®BioTechLogic, Inc.
Slide 2
BioTechLogic was founded by former Searle/Pharmacia/Pfizer
colleagues.
BioTechLogic, Inc. provides biopharmaceutical manufacturing and
regulatory consulting services with strategic and hands-on experience
to assist clients in bringing their products to market by augmenting
and optimizing an organization’s resources.
Our Expertise is Process Validation, CMC Regulatory Services and
Quality Systems.
About BioTechLogic, Inc.

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LOGICBIO TECH
Slide 3
Did you Know Since 2004 …
 Our Company experience includes over 300 different projects with 50+ different
clients.
 We have supported 22 different biologically derived compounds, including 11
recombinant proteins, 4 vaccines, 3 antibodies, 2 blood based-products, 1 fusion
protein and 1 pancreatic enzyme.
 We have collaborated on 7 synthesized macromolecules, including 5 Oligonucleotide
and 2 peptide projects.
 BioTechLogic has led 9 Process Validation Programs, 6 of which have been
submitted and accepted by the FDA and/or EMA to date.
 Involvement with 2 FDA approved Biological products (BLA’s), 2 FDA approved
Drug Products (NDA’s), 2 FDA approved Drug/Device Combination Products
(PMA’s), and another Biologic which just received a positive opinion from EMA (MAA).
 We have served as the primary CMC author for 5 IND’s, 5 BLA’s, 5 MAA’s and
1 IMPD.
 2 small molecule compounds, including a solid oral dosage and a parenteral.
About BioTechLogic, Inc.

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BioTechLogic’s Expert Services
• Manufacturing: On-Site Third Party Contract Manufacturing Support,
Statistical Process Controls, Supply Assurance
• Project Management: Project Plans and Schedules, Project Management,
Team Organization, Meeting Management, Action Item Tracking, Budget
Tracking
• Process Validation: Strategy, Critical Process Parameter Evaluation,
Validation Protocols and Reports, Support Validations (Resin Re-Use,
Membrane Re-Use, Hold times, Loading Studies, etc.)
• Quality Assurance: Audits, Global Inspection Readiness, Quality Systems,
On-Site Senior Management
• Supply Chain: Site Selection, Make vs Buy, Due Diligence
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BioTechLogic’s Expert Services
• Combination Products: Regulatory Strategy, Dossiers, CE Marking
process
• Regulatory Submissions: Briefing Packages, Agency Responses,
IND’s/IMPD’s, BLA/MAA, PAS or ROW Filings, 510K, PMA’s
• Process Development: Development Reports, Design of Experiments
(DOE), Technology Implementation, Transfer and Scale-up, Comparability,
Troubleshooting, Technology and Facility Evaluations
• BioAnalytical Services: Strategy, Hands-On, Expert Review, Analytical
Method Transfer
Slide 5

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Slide 6
The BioTechLogic Advantage
Our success is due to our ability to serve as the technical expert and consultant,
yet be the hands-on resource that drives and executes the plan.
Most Ex-FDA and Consultants
Our service advantage is that we have depth and breadth of knowledge. Our staff
began their careers in R&D or on the Manufacturing floor, and has a depth of
understanding that goes beyond typical consultants. We have worked on many
different types of technologies to benchmark best practices, and have taken numerous
biopharmaceutical drugs through commercialization.

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Biologic Process Validation Experience
– 2002: Somavert® (Growth Hormone Antagonist) - PEGylated Protein [FDA and EMA Approval 2003]
• API and Drug Product PV Strategy and PV Support Documentation Generation
– 2003: Macugen® (pegaptanib sodium injection) - PEGylated Oligonucleotide [FDA Approval 2004 and
EMA Approval 2005]
• API PV Strategy, CMO Site PV Readiness and PV Support Documentation Generation
– 2004-2005: FAb - PEGylated Antibody Fragment [FDA Approval 2008 and EMA 2009]
• API and Drug Product PV Strategy, CMO Site PV Readiness and PV Support Documentation
Generation
– 2006-2008: PolyHeme® - a Novel Therapeutic Hemoglobin (Oxygen Carrier) [FDA 2009 No CMC Issues]
• API Process Validation Gap Assessment
• API PV Readiness Support, Development and Comparability Reports
• 2009; Product not approved by FDA citing clinical deficiencies. No CMC issues.
– 2007-2008: Asparaginase - Recombinant Protein [EMA 2011 submission]
• On-Site Analytical Support, Support Validations, and PV Project Management
– 2008: Vaccine - National Defense Program
• Process Validation Responses to NIH, BARDA and FDA
• Process Validation Strategy for API and Drug Product, and PV Support Documentation Generation
Slide 7

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Biologic Process Validation Experience
– 2009: Vaccine
• PV Strategy, Site PV Readiness as well as Support Validation Documentation and PV Protocols for
Adjuvant, API and Drug Product
• Qualification Batches completed in 2009
– 2011: Generic Fusion Recombinant Protein (on-going)
• PV Strategy, Support Validation Documentation and Drug Substance PV Protocols
• Qualification Batches to be completed in 2011
– 2011: Generic Small Molecule Injectable (pending)
• PV Strategy, Site PV Readiness as well as Support Validation Documentation and Drug Product PV
Protocols
• Qualification Batches to begin in 2011
– 2011: Generic Recombinant Protein (pending)
• PV Strategy, Site PV Readiness as well as Support Validation Documentation and PV Protocols Drug
Substance and Drug Product
• Qualification Batches to begin in 2011
Slide 8

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BioTechLogic, Inc.
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Applying QbD to Biotech Process
Validation:
A case study in applying QbD to
Stage 1 – Process Design
Slide 9

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Slide 10
The scope of this case study is to provide and evaluate
data obtained from small-scale Ion Exchange
Chromatography runs performed within defined parameter
limits in experiments planned and executed according to
Design of Experiments (DOE).
Process Background

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Process Background
Slide 11

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Risk Assessment
Slide 12
Operating Parameter Normal Operating Range Potential Effect(s) of Failure SEV OCC DET RPN
UV at Start of AEX Fraction Collection > 0.50 AU Product loss, quality 9 4 2 72
UV at End of AEX Fraction Collection < 20% of maximum peak height Product loss, quality 9 4 2 72
Elution Gradient
8% per CV
(20 – 100% B over 10 CV)
Inconsistent quality 8 2 4 64
Equilibration volume ≥ 5 CV Product loss, quality 9 2 1 18
Load flow rate 100 – 120 L/h (Target: 110 L/h) Inconsistent quality 4 1 3 12
Wash flow rate 100 – 120 L/h (Target: 110 L/h) Inconsistent quality 4 1 3 12
Elution flow rate 100 – 120 L/h (Target: 110 L/h) Inconsistent quality 4 1 3 12
Pre-Equilibration flow rate 100 – 120 L/h (Target: 110 L/h) Longer equilibration 4 1 3 12
Fraction Volume
F1 – F3: 0.2 CV
F4+: 0.1 CV
Product loss, quality 9 1 1 9
Wash volume ≥2 CV Inconsistent quality 5 1 1 5
Fraction Mixing Speed 65 rpm Inconsistent quality 4 1 1 4
Fraction Mixing Time 20 - 30 min Inconsistent quality 4 1 1 4
Pool Mixing Time
10 – 15 min
Non-homogeneity, inconsistent
sampling/yield
4 1 1 4
Pool Mixing Speed
100 rpm
Non-homogeneity, inconsistent
sampling/yield
4 1 1 4
Pre-Equilibration volume ≥ 4 CV Longer equilibration 3 1 1 3
WFI Rinse volume ≥ 3 CV Longer equilibration 3 1 1 3
WFI Rinse flow rate 100 – 120 L/h (Target: 110 L/h) Longer equilibration 1 1 1 3
Equilibration flow rate 100 – 120 L/h (Target: 110 L/h) Product loss, quality 3 1 1 3
In-Process Control Limits
Column Load 15 – 25 g / L resin
Column Bed Height 30 ± 3 cm
Column Backpressure during
Equilibration, Load, Wash, and Elution
< 3 bar
Effluent pH at end of Equilibration
± 0.3 pH Units of Equilibration
Buffer pH
Effluent Cond. at end of Equilibration
± 1 mS/cm Units of Equilibration
Buffer Conductivity
Effluent UV at the end of Equilibration Zero
Fraction Pooling Criteria (RP-HPLC) ≥ 95% Main Peak

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Risk Assessment
The parameters selected for evaluation in this study were
the UV at start of fraction collection, UV at end of fraction
collection, gradient slope, and resin load.
Dependent variables analyzed were protein recovery yield
and purity by RP-HPLC.
Slide 13
Input parameter
Testing limits
Output parameters
Lower Upper
UV at the beginning of
fraction collection (AU)
0.40 0.60
Purity by RP-HPLC (≥ 95%)
Step yield (≥ 75%)
UV at the end of pooling
(% from peak max)
10 30
Gradient slope (% per CV) 4.0 12.0
Resin Load, (g/L resin) 15 25

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DOE
Slide 14
Pattern Exp. No.:
UV at the start
of fraction
collection
(AU)
UV at the end
of pooling
(% peak max)
Gradient
slope
(% per CV)
Resin Load
(g/L)
−−−− 1 0.4 10 4 15
0000 2 0.5 20 8 20
0000 3 0.5 20 8 20
−++− 4 0.4 30 12 15
0000 5 0.5 20 8 20
++−− 6 0.6 30 4 15
+−−+ 7 0.6 10 4 25
++++ 8 0.6 30 12 25
+−+− 9 0.6 10 12 15
−−++ 10 0.4 10 12 25
0000 11 0.5 20 8 20
−+−+ 12 0.4 30 4 25
Note: It is important to randomize the run order to assure the
independence of your observations and reduce the chances of a
mistake by the operator.

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Experimental Results
• A summary of the variable parameters for each of the
Ion Exchange Chromatography runs as well as the
Outputs, step yield and eluate purity by RP-HPLC are
listed in the following table.
• Runs that did not meet the defined acceptance criteria
are shaded to indicate run failure
Slide 15

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Experimental Results
Slide 16
Run
Number
Resin Load
(g/L)
Gradient
Slope (%)
UV @ Start
Collection
% Peak
Height End
Collection
Step Yield
(%)
RP-HPLC
Purity (%)
Acceptance Criteria
≥ 45% ≥ 95%
1 15 4 0.4 10 53 95
2 20 8 0.5 20 56 97
3 20 8 0.5 20 50 97
4 15 12 0.4 30 59 96
5 20 8 0.5 20 51 97
6 15 4 0.6 30 53 97
7 25 4 0.6 10 52 96
8 25 12 0.6 30 40 97
9 15 12 0.6 10 50 98
10 25 12 0.4 10 45 97
11 20 8 0.5 20 51 97
12 25 4 0.4 30 60 97

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Statistical data analysis
• For evaluation of statistically significant effects of factors
on protein quality and quantity, the JMP statistical
software package was used applying the Standard Least
Squares model for Effect Leverage.
• The model was run separately for two output parameters
of yield and purity by RP-HPLC.
• The fractional factorial model was initially run with the
inclusion of all single factor and some two-factor
interactions as model effects (Resolution IV). Due to
resource and time constraints, a higher resolution study
was not possible.
Slide 17

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Statistical Model Parameter Estimates for
Step Yield
• The final model used for assessment all four primary
effects and two factor interactions listed above.
• There were two significant main effects and one
significant interaction. These conclusions were
graphically represented in the main effects leverage
plots and interaction contour plots.
Slide 18

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Main Effect Leverage Plots for Step Yield
Slide 19
a. UV at Start Collection b. UV at End Collection
c. Gradient Slope d. Resin Load

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Statistical Model Parameter Estimates
for Purity by RP-HPLC
• The final model used for assessment included all four
primary effects (excluding resin load) as well as three
two-factor interactions.
Slide 20

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Two-Factor Interaction Contour Plots
for Purity by RP-HPLC
Slide 21
a. UV at End Collection x Gradient Slope b. UV at Start Collection x Gradient Slope
0
UV @ End (%)
Gradient Slope (%/CV)
Step Yield (%)
0
UV @ Start (AU)
Resin Load (g/L resin)
Step Yield (%)

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Confirmation of Results
• There is one area of the tested design space that represents process
failure space with regard to step yield, run 8. In order to ensure that
the step yield acceptance criterion is always met when the process is
operated within its defined PARs, the design space was modified
and two additional runs were performed to augment the initial
design.
• We chose to tighten the gradient slope upper limit to 11% as it was
determined to be easy to control and the most significant primary
effect for step yield in our initial analysis.
• None of the experimental runs for the tightened limits failed the
acceptance criterion for step yield. This confirms the predicted
design space from the initial study.
Slide 22

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Design Space vs. Defined Control Space
Based on the models
generated for each of the
defined process outputs, a
three-dimensional plot was
generated to graphically
show the process control
space with regard to the
experimentally defined
design space
Slide 23

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Conclusions
• The results of this DOE study confirm the predicted
design space from the initial study.
• Based on our findings we determined that the IPC limits
we chose for the three input parameters were
appropriate with the exception of Gradient Slope.
• As a result we tightened our control limits.
• The IPC limits for resin load were also determined to be
set appropriately.
Slide 24

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Final Parameter Limits
Slide 25
Operating Parameter
Normal Operating Range
(NOR)
Experimentally Confirmed
Ranges
UV at the beginning of
pooling, % from peak max Target: > 0.5 AU 0.4 – 0.6 AU
UV at the end of pooling, %
from peak max Target: 20% 10 – 30%
Gradient slope, % per CV Target: 8% 4 – 11%*
Performance Parameter In-Process Acceptance Criterion
Resin Load, g/L 15 – 25 g/L
* Note: The claimed experimentally confirmed range is slightly tighter than the design space in that it takes the worst-case data point to
support the higher end of the range.

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BioTechLogic, Inc.
BIO TECH
LOGICBIO ®
Process Validation
Overview
Slide 26

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Slide 27
Lifecycle Approach to Process Validation
“The lifecycle concept links product and process development,
qualification of the commercial manufacturing process, and
maintenance of the process in a state of control during routine
commercial production.”
- FDA Guidance for Industry, Process Validation: General Principles and Practices
(January, 2011)
“Process validation involves the collection and evaluation of
data, from the process design stage throughout production,
that establish scientific evidence that a process is capable of
consistently delivering a quality drug substance.”
- ICH Q11, Development and Manufacture of Drug Substances
(May, 2011)

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Slide 28
Three Stages of Process Validation
A high degree of assurance in the
performance of the manufacturing
process must be attained prior to
commercial distribution (i.e., “initial”
validation)
Ongoing assurance of the “validated
state”…as materials, equipment,
production environment, personnel,
and manufacturing procedures
change.

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• The “burden of proof” is not merely three successful,
consecutive conformance batches, but rather a
manufacturer’s high degree of assurance.
• How does one gain a high degree of assurance in a
manufacturing process?
 Understand sources of variation
 Detect the presence and degree of variation
 Understand the impact of variation on the process and on product
attributes
 Control the variation in a manner commensurate with the risk it
represents to the process and product
- FDA Guidance for Industry, Process Validation: General Principles and Practices
(January, 2011)
Slide 29
Burden of Proof

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The BioTechLogic Approach to PV
Slide 30
• Understand Function of Each Unit Operation
• Define Process Inputs and Outputs
• Determine Potential Critical Operating Parameters, Steps or Raw
Materials by Risk Assessment
• Experimentally define NOR and PAR for critical parameters using
DOE and Statistical Assessment
• Based on DOE studies, establish final list of Critical/Key Operating
Parameters
• Parameter Evaluation / Pre-Qualification Report
• Execute Support Validation Studies to Define Ranges for Process
Definition (Holds, Re-use, etc)
• Final Process Control Strategy
• Define the Process by Finalizing the Batch Record
• Utilities, Facility, Equipment, Components, Raw Materials and
Analytical Readiness
• Write Process Performance Qualification Protocol(s)
• Execute Process Performance Qualification Batches
• Write Process Performance Qualification Report(s)
• Create Continued Process Verification strategy
• Compile and assess CPV data after each batch
• Maintain/Assess Qualified Process via appropriate CAPA and Change
Control
Process Design
Process
Qualification
Continued Process
Verification
ProcessValidation

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Slide 31
A Bird’s Eye View of Process Validation

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Slide 32
Map of Typical API Process Validation

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Slide 33
Map of Typical DP Process Validation
Process Data
Trending
Process
Characterization
Studies
Infusion bag
Extractable/
Leachable
Filter Validation
Fill Accuracy
Mixing Study
Post Filtration
Processing Time
Pre-Filtration Hold
Time
Shipping Validation
Utility
Qualification
Equipment
Qualification
Equipment
Cleaning
Validation
Raw Material
Analysis
Vendor
Qualification
Component
Compatibility
In-Process
Method
Validation
Release
Methods
Validation
Analytical Method
Qualification/
Validation
Raw Materials &
Component
Qualification
Facility
Qualification
Process Control
Strategy
Commercial
Manufacturing
Instructions
Process
Qualification
Continued
Process
Verification

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How Can BTL Help You Succeed (Stage 1)?
• Define Process Inputs and Outputs
– Review production batch records to compile comprehensive list of process parameters
– Write Parameter Justification Report to define how parameters and ranges were established
– Write Process Development Reports to appropriately capture process knowledge
• Determine Potential Critical Operating Parameters, Steps or Raw Materials by Risk Assessment
– Lead comprehensive, multi-disciplinary process risk assessment to rank potential sources of process
variation
– Write Risk Assessment Reports throughout clinical development (lifecycle approach means risk
assessment are being re-visited as process knowledge increases)
• Experimentally define NOR and PAR for critical parameters using DOE and Statistical Assessment
– Design an experimental approach that will ensure a risk-based and science-based approach to determining
parameter criticality
– Establish and qualify appropriate scaled-down models
– Ensure that DOE studies are appropriately designed and interpreted, with experience in JMP, Design-
Expert, and MiniTab statistical software
– Execute Quality by Design
• Based on DOE studies, establish final list of Critical/Key Operating Parameters
– Facilitate decision-making with regard to formal PPQ acceptance criteria
– Implement PDA Technical Report 42 approach for parameter designation to ensure consistency with
industry standards
Slide 34

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Slide 35
• Parameter Evaluation / Pre-Qualification Report
– Write a comprehensive Parameter Evaluation Report as the “roadmap” to all the various risk assessments
and process characterization reports
– Add benefit in that, if formatted appropriately, this document forms the basis for BLA/MAA Section 3.2.S.2.4
• Execute Support Validation Studies to Define Ranges for Process Definition
– Design, review, and/or write protocols and reports for process validation support studies, including resin
lifetime studies, membrane lifetime studies, media hold studies, in-process hold studies, buffer hold studies,
process-related impurities removal studies, filter validation, container/closure validation, etc.
• Final Process Control Strategy
– Write a Process Control Strategy, which is a summary of results from the validation support studies and the
process characterization work, and provides the critical link between Stages 1 and 2 of Process Validation
• Define the Process by Finalizing the Batch Record
– Ensure that the highest quality batch records are carried forward into Process Performance Qualification
(Stage 2) based on extensive scientific/process and quality/regulatory experience

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Slide 36
• Utilities, Facility, Equipment, Components, Raw Materials and Analytical Readiness
– Ensure that appropriate risk assessments and qualifications are in place for production facilities, utilities,
and equipment
– Ensure that appropriate raw materials and components risk assessments and vendor qualifications have
been performed
– Ensure that appropriate analytical method qualification has occurred prior to Stage 2 Validation
• Write Process Performance Qualification Protocol(s)
– Ensure that appropriate assessment of historical data (both small-scale and production-scale) has been
completed to define the appropriate number of PPQ batches to define in PPQ protocol(s)
– Ensure that PPQ approach is appropriately connected back to process knowledge gleaned during Stage 1
Validation
• Execute Process Performance Qualification Batches
– Provide Man-in-Plant support prior to and/or during PPQ campaign to facilitate communication between
client and manufacturing facility and to ensure immediate oversight of production and to provide
troubleshooting capabilities
• Write Process Performance Qualification Report(s)

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Slide 37
• Create Continued Process Verification strategy
– Guide a multi-disciplinary team through a risk-based and science-based approach to a step-down plan from
PPQ sampling and testing strategy
– Write a CPV Master Plan/Strategy Document
– Guide manufacturers in implementation of sponsor’s CPV plan
– Review manufacturers quality systems to ensure appropriate mechanisms are in place to feed appropriate
data/information from systems into overall CPV assessment
• Compile and assess CPV data after each batch
– Ensure appropriate systems are in place at manufacturer to compile CPV data/information and to
communicate the “validated state” across the organization in a timely manner
• Maintain/Assess Qualified Process via appropriate CAPA and Change Control
– Review change control documentation to ensure compliance with regulatory expectations

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Slide 38
The “Heart” of Process Validation

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Post-Approval Process
Validation Reporting
Slide 39

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Introduction
Slide 40
• Post-validation reporting requirements essentially have
not changed. The PV Guidance document repeatedly
cites CFR 211.180(e), which is a 30 year-old regulation.
• What’s “new” is inherent in the name of Stage 3 –
Continued Process Verification and emphasized in the
first line of Section IV.D in the guidance document:
“The goal of the third validation stage is continual assurance that the
process remains in a state of control (the validated state) during
commercial manufacture.” (FDA’s Guidance for Industry – Process
Validation: General Principles and Practices, 2011)

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Two Categories of CPV Information
Slide 41
Category #1 = Process/Product Data Category #2 = Events-Based Data
• Process trends
• RM/Component
Quality
• In-Process
Material Quality
• Finished Product
Quality
• Defect complaints
• OOS findings
• Process deviation
reports
• Operator
observations
• Batch record reviews
• Adverse events
reports

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Goals of a Comprehensive CPV Program
1. To combine the evaluation of process data and events-based data
so that process and product are constantly “informing each other”.
2. To review both types of data in a “continual” manner so that it’s not
too late to take action to minimize patient risk and save batches.
3. To document such data according to good process knowledge
management principles so that valuable information is not lost.
4. To effectively communicate such information across the
organization so that quick decisions can be made to ensure patient
safety.
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How Do We Implement CPV Objectives?
• Leverage information gleaned during Stage 1
and Stage 2 validation to minimize the “burden
of proof” for Stage 3.
• More information is not always better. Be
selective (based on risk-based and science-
based “filters”).
• Utilize existing quality systems. But, again, be
selective about what gets fed into the CPV
program.
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CPV Plan Development
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Key Point
Post-Approval Process Validation Reporting
is more about getting key personnel within an
organization informed and on the same page
regarding the state of process control than it
is about communicating that information to
the regulatory authorities. CPV
documentation will likely form the basis for
your APR reports, but that is not their primary
objective. Your CPV Program should reflect
this internal focus.
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Considerations for Implementation
Raw Materials & Components:
• Are existing specifications adequate for ensuring tight control of
material quality going into the process?
• Even if adequate specifications are in place, have we reviewed
historical material quality to see what kind of variability we have
within the defined specifications and whether or not such variability
has the potential to impact product quality?
• Have component material of construction compatibility and
leachables/extractables studies adequately ensured low risk to
product quality? If not, is there additional testing that can be done
as part of CPV to mitigate risk?
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In-Process Data:
The PPQ Protocols and Reports are a good starting point for the in-
process data section of the CPV plan since (in theory) a company has
already “funneled” all process parameters through risk assessment
and, if needed, characterization (again, leveraging Stage 1 & 2
information to inform Stage 3). In addition, the PPQ protocols will have
identified any additional sampling/testing required for validation and
those should also be considered for Stage 3.
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In-Process Data:
• Is this parameter subject to normal distribution and
trending?
• Did we gain a sufficient statistical confidence in our
process during PPQ to drop any additional testing?
• If we didn’t gain statistical confidence from our additional
testing during PPQ, then how many more batches will it
take to do so?
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Release Data:
• This one usually isn’t as much of a problem,
because data compilation and communication
are required for lot release.
• However, just as some in-process data are
subject to statistical trending and others are not,
so it is the same with release data. Such
distinctions should be identified in the CPV Plan.
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What Would Output Look Like?
Slide 50
Critical Operating Parameter Normal Operating Range CPV Strategy
Carbon feed rate Target ± 15%
Not subject to SPC; however, important to monitor controller
variability, so will plot glucose feed profile for comparison
(overlay) with previous batches
In-Process Control Limit(s) CPV Strategy
DO ≥ 10%
Not subject to SPC; characterization data shows that any value
above 10% is sufficient for consistent fermentation
performance; no routine trending required, but any IPC failure
will be fully investigated for impact to the validated state
Total Acid Addition Volume ≤ 6.0 kg
Known to have direct impact on product quality and subject to
SPC; parameter will be control charted
In-Process Acceptance Criterion Acceptance Criterion CPV Strategy
Final Wet Cell Weight > 250 g/L
Important indicator of process consistency and subject to SPC;
parameter will be control charted
Culture Purity (Plating Method) No Contaminating Organisms
Not subject to SPC; existing quality systems would ensure that
a failure would result in batch termination.
Final Product Titer ≥ 3.0 g/L
Important indicator of process consistency and subject to SPC;
parameter will be control charted
Additional Testing Acceptance Criterion CPV Strategy
Antifoam Concentration Report Value
This result is used as a baseline value for assessing antifoam
clearance further downstream; PPQ batches demonstrate tight
control of antifoam clearance to < LOQ, so testing will be
discontinued.

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Events-Based Data/Information:
• Are the appropriate systems in place to thoroughly
assess events-based issues like deviations, OOS
results, operator observations, change control, adverse
events, etc.?
• If they are already in place, then the project team should
be focused on ensuring that all of those various pieces
of information are being fed back into a CPV system that
ensures timely communication of process/product risk
across the organization.
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CPV Reporting Mechanisms
• The types of data/information that will be formally
assessed via CPV should be defined by a cross-
functional team and documented by way of a CPV Plan.
• Regardless of the product type, it should be clear that
process data/information are being evaluated on a
continual basis and that outcomes are being formally
documented and communicated across the organization
on a more frequent than annual basis.
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Considerations for Legacy Products
• In some ways, legacy products have an advantage in
that there is an existing historical data set that should be
conducive for establishing SPC limits.
• So, walk through the same CPV exercise that we just
discussed:
– Identify any high risk raw materials and components and take
appropriate mitigating actions.
– Review Process Control Strategy and determine which data are
subject to trending and which are not.
– Review quality systems and determine which events-based data
will be compiled and reported as part of CPV
– Develop a CPV Plan to ensure timely reporting and
communicating CPV data/information across the organization.
Slide 53

TECH
LOGICBIO TECH
Conclusion
Don’t default to assessing and reporting everything about
your process in Stage 3. Instead, utilize the risk-based and
science-based “filters” established during Stage 1 and 2
and rely on existing quality systems to ensure that the right
set of data are being monitored, and then ensure that a
system is in place to communicate that information across
the organization.
Slide 54

Process Validation Presentation from BioTechLogic

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Process Validation Presentation from BioTechLogic