Process Performance Qualification
Demonstrating a High Degree of Assurance in Stage 2 of the Process Validation Lifecycle
A LIFECYCLE Approach to Process Validation?
The Validation Group Management
Lifecycle [ICH Q8(R2)]: All phases in the life of a product from the initial development through marketing until the product’s discontinuation.
Medical Devices
• Global Harmonization Task Force Process Validation Guidance Reference
• The Power of Process Validation in Devices Stage 1 – Process Development
• Large Molecule Development: Always an Enhanced Approach
• Enablers
Stage 2 – Process Performance Qualification
• A High Degree of Assurance
• PPQ: What could possibly go wrong?
Stage 3 - Continued Process Verification
• Leveraging Quality Planning to Achieve High Level of Assurance The views expressed are solely those of the presenter
“Quality Management Systems – Process Validation Guidance”
Global Harmonization Task Force – Medical Devices
Referenced in US FDA Guidance for Industry “Process Validation: General Principles and Practices” January 2011
Similarities between GHTF and FDA Guidances
• Similar lifecycle approach
• Use of statistical methods emphasized
• Robust Quality Systems expected to support the an on-going state of control
“The product should be designed robustly enough to withstand variations in the manufacturing process…process should be capable and stable to assure continued safe products that perform adequately”
“Process Validation is conducted in the context of a system including design and development control, quality assurance, process control and corrective and preventative action.”
“Process Validation is a term used in the medical device industry to indicate that a process has been subject to such scrutiny that the result of the process …can be practically guaranteed”
Auto-Injector Components:
A High Degree of Assurance
Injection Molding (Theoretical Example) 12 cavity mold (each cavity = 1 part) 120 second cycle
Tool Qualification: Dimensional Inspection
1 part X 0.5 cycles X 60 minutes = 30 parts cycle minute hour hour
Cycle Validation X 3:
Parameter range high, midpoint, low
12 parts X 0.5 cycles X 60 minutes = 360 parts cycle minute hour hour
A High Degree of Assurance
Medical Devices
• Design and Development Controls
• Process Validation (IQ, OQ, PQ)
• Monitor and Control / Revalidation
Engineering Focus: Adequate component sample sizes = Heavy reliance on statistical methods
Biopharmaceuticals
• Development
• Process Qualification
• Continued Process Verification
Life Science Focus: Biological systems, few data = Additional measures where statistics alone may be impractical.
“Each manufacturer should judge whether it has gained sufficient
understanding to provide a high degree of assurance in its manufacturing process to justify commercial distribution of the product.”
• Stage 1 – Which and how much data can be used in conjunction with PPQ data to provide confidence the continuing process control?
• Commercial Manufacturing – How much commercial scale data is needed?
• Established platform manufacturing - Less?
• Contract manufacturing organizations – More?
• Quality System - Can the quality system support an ongoing state of control?
• Has Stage 1 process and product knowledge been integrated into the system?
Stage 1
Development
Stage 2
Process Qualification
Stage 3
Continued Process Verification
High Degree of Assurance at End of Stage 2
Analytical Characterization ICH Q6B
Process Development and Characterization
ICH Q8
Risk and Criticality Assessments ICH Q9
Cell Line Qualification ICH Q5A, Q5B, Q5D
Comparability ICH Q5E Stability Testing
ICH Q5C Clinical Manufacturing
ICH Q7
The complexity of the molecule and manufacturing processes have necessitated enhanced approaches to development
Stage 1: An Enhanced Approach in Biopharma
Complex Structure and Properties
1º 4º
2º 3º
Physiochemical Properties
• Structural Heterogeneity
• Post-translational Modifications
• Product Related Substances Biological Activity
• Higher Order Structure
Immunochemical Properties
“Since the heterogeneity of these products defines their quality, the degree and profile of this heterogeneity should be characterized to assure lot to lot
consistency.” ICH Q6B
Impurities
• Process Related Impurities
• Product Related Impurities
• Degradation Products Contaminants
• Endogenous Virus
• Adventitious Agents
Quality Attributes can be influenced by
Molecular Design, Process Design, and Process Control
It’s all about Control Strategy
Specifications / Release testing
• Clinical Justification most important
• Criticality, process capability and delectability
Analysis and Characterization
• Process characterization
• Extended product characterization / comparability
Process Control and Monitoring
• Process and product impurities
• Raw materials
• Process monitoring / in-process testing
• Controls, set points, ranges, hold times
• Process qualification / validation
• Process Data Tracking and Trending
Derived from: S. Kozlowski, P Swann / Advanced Drug Delivery Reviews 58 (2006)
UNKNOWN
Communicating a High Degree of Assurance
Enablers:
• Standardized Terminology
• Knowledge Management
• Quality Systems – Quality Planning
Perspective on Standardized Terminology
“it was recognized from both industry and
regulators that there is a need for standardized terminology and use of ICH nomenclature when present. There might be a need for additional terms such as….”
A-mAb Product Lifecycle
A-mAb: Criticality Continuum
Quality Attributes
In development, the degree of criticality may be assigned to quality attributes based on potential safety and efficacy consequences. Following comprehensive assessments of scientific evidence and risk, quality attributes are ranked according to the degree of criticality.
Avoids “non-critical” terminology which may suggest uncontrolled.
High Criticality Quality Attributes
The continuum, as opposed to binary classifications of Critical and Non-Critical, is thought to “more accurately reflect complexity of structure-function relationships and the reality that there is some uncertainty in
attribute classification” Low Criticality
Quality Attributes
Quality Attributes: No “NONs”
ICH Q5E: Quality Attribute
A molecular or product characteristic that is selected for its ability to help indicate the quality of the product. Collectively, the quality attributes define identity, purity, potency and stability of the product, and safety with respect to adventitious agents. Specifications measure a selected subset of the quality attributes.
Quality Attributes Critical
Quality Attributes
ICH Q6B: Product-Related Substances
Molecular variants of the desired product formed during manufacture and/or storage which are active and have no deleterious effect on the safety and efficacy of the drug product. These variants possess properties comparable to the desired product and are not considered impurities.
A-mAb Process Parameter Classification
Reproduced/Derived from A-mAb Case study
Process Performance
“Input parameters that must be controlled within a narrow range and are essential for optimum process performance.”
Key process parameters do not affect critical quality attributes.
Standardized Terminology: Control and Criticality
?
If a parameter controllability is high
risk even within the design space, can this be considered a
state of control?
Should a robust control strategy
provide
assurance that all process
parameters are well-controlled?
Process Control Strategy Vocabulary
Control
Can the variable be controlled?
No
Process Output
Process Performance Attribute or
Product Quality Attribute
Process Variable
Yes
Process Input
Process Parameter
Functional Relationships and Parameter Classification
Critical Process Parameters Critical Quality Attributes
Key Process Parameters Process Performance Attributes Non-Key Parameters Low Risk of Impact
Process Performance Attributes
Process performance monitoring: Maintaining a state of control
• Monitoring of product quality attributes alone incomplete - changes in process performance may represent “early warning sign”
• Monitored, tracked, trended in Continued Process Verification
• Process performance attributes demonstrate inter-batch consistency
Production Bioreactor
Key Parameter:
Osmolality
Performance Attribute:
Antibody Titer
IEX
Chromatography
Key Parameter:
Load Conductivity
Performance Attribute:
Recovery
Documentation and Knowledge Management
“In all stages of the product lifecycle, good project management and good archiving that capture scientific knowledge will make the
program more effective and efficient.”
Turning Documents into Knowledge
Engaging the Quality Unit early can be a wise investment in managing documents and knowledge!
QA?
Engage the Quality Group to enable knowledge management
• Comprehensively communicating a high degree of assurance through PPQ reports and in S.2.5 is more likely
• Ensure knowledge integration into the quality system (ICH Q10)
Documentation and Knowledge Management
Development Reports Analytical
Reports Batch Records Qualification
Reports
Technical Summary
Process Development
Product Characterization
Pilot Scale Production
Robustness Studies
Risk Assessment
Lifecycle Document
FMEA Report
PPQ Protocols and Reports: Comprehensive Story
PPQ documents as tools to describe a high degree of assurance
• Provide a comprehensive description of the control strategy.
– Include “non-critical” process variables even though only a subset of parameters and attributes will comprise PPQ
• Describe how the subset of PPQ parameters and attributes demonstrates a state of control
• Reference appropriate stage 1 data and discuss relevance.
• PPQ Acceptance Criteria
– How established and why
TELL THE WHOLE STORY / MAKE NO ASSUMPTIONS
Stage 2: High Degree of Assurance
Qualification of Facilities, Utilities, and Equipment Contamination Control Strategy
• Facilities Flow and segregation
• Equipment Preventative Maintenance
• Procedures Changeover
• Monitoring Environmental, Process Gas, Water
• Validation
• Cleaning and Sterilization
• Membrane & Resin Lifetime
• Bioburden & Endotoxin Limits (and on-going monitoring)
Specifications, Acceptance Criteria,
Action Limits
Product Characterization
Quality Systems and GMP
Raw Materials Analysis
In-Process Testing Stability
Testing Release
Testing
Process Controls and Monitoring
Qualification of Process Performance: Process Control Strategy
PPQ Not Limited to Stage 2
• Scaled down predictive, qualified models
– Viral Spiking Studies – (ICH Q5) Stage 1 – Process Robustness – (ICH Q8) Stage 1 – Impurity Clearance – (ICH Q8) Stage 1, 2
– Chromatography Resin Lifetime – Stages 1, 2, 3
• Extended Analytical Product Characterization
– Structure Function Relationships (ICH Q6B) – Stage 1 – Comparability (ICH Q5E) – Stages 1, 2, 3
• Real Time (Parametric) Release
– Viral inactivation and clearance parameters Stage 3 – Impurity clearance: DNA, Protein A Stage 3
Enhanced
Sampling During PPQ
Filtration Viral
Inactivation
Cation Exchange
Capture Viral
Removal Filtration
Anion Exchange
Filtration
Routine Samples
Characterization-Demonstrates comparability Impurity Clearance – Validates small scale models Protein Stability – Qualifies non-microbial hold time
Perspective on Enhanced Sampling
Enhanced sampling and testing to be discontinued after PPQ:
• PPQ is fully supportive of the predictive small scale models (impurities:
Protein A, DNA)
Enhanced sampling to continue:
• Unexpected results obtained in PPQ
• Trends suspected in PPQ data
Plan for data collected FIO (significant variability estimates):
• Rationale for continued sampling
• Plan for evaluation of accumulated data
• Timeframe or amount of data needed to for decision on continuation.
“We recommend continued monitoring and sampling …at the level established during the process qualification stage until sufficient data are available to generate significant variability estimates”
Use of Statistical Methods at End of Stage 2
Likely to rely on means other than statistics alone to achieve a high degree of assurance
Often insufficient data to correctly apply traditional statistics.
• Few clinical batches
• Limited number of commercial scale batches
• Statistically based sampling plans not useful for homogeneous bulk pools
Achieving a high degree of assurance with limited use of statistics requires clear, comprehensive rationale with references to supporting studies conducted in Stage 1.
Quality Planning for Commercial Manufacturing What to Measure, Where to use Statistics
Inoculum Expansion
Seed Bioreactors
Production Bioreactor Thaw
Quality Plan / CPV Plan finalized at end of Stage 2.
• What is to be measured and why, accounting for interactions
• Statistical methods to be used for data evaluation.
• Frequency with which data will be evaluated
• Frequency of Management Review
Action Limits and Acceptance Criteria Statistical Monitoring
Feed Rate / Volume increased
after 1st PPQ run to to increase
titer.
What next?
Production Chromatography Operations Drug Substance
Bioreactor Titre (2.7 – 4.0)
Recovery Capture (70-100)
Recovery AEX (90-100)
Recovery CEX (90-100)
Acidic Variants
(25-35)
Oxidation (3-10)
Aggregate
<4%
Process Performance Attributes Quality Attribute
Critical Quality Attributes
Pilot 1 3.5 97 99 80* 25 10 2.0%
Pilot 2 3.9 95 99 90 30 5 3.1%
Pilot 3 3.0 93 95 99 28 7 2.6%
Pilot 4 3.2 91 92 92 27 5 3.0%
Pilot 5 3.8 98 100 97 30 10 1.9%
Eng 2.6 86 95 98 28 8 3.0
PPQ 2.7 89 98 90 22 7 2.0%
PPQ 3.5 90 97 95 23 9 2.2%
PPQ 3.2 91 96 89 25 9 1.8%
Unexpected Results in PPQ
Unexpected PPQ Results: High Degree of Assurance in Continued Process Verification
“… a reduced number of batches cannot adequately capture the expected process variability at commercial manufacturing scale.
To provide continued assurance that the process remains in a state of control throughout the life of commercial
manufacturing, we will create a multivariate statistical partial least squares model (PLS) as part of continued process
verification.”
Appropriate Statistical Methods
“PLS is more powerful than standard univariate Statistical Process Control (SPC) approaches in that it ensures that the internal correlations among the different variables are also considered. For example if at any given time the titer is lower than expected for the measured viable cell concentration, the PCA model will be able to detect this as a potential out of norm signal even if both parameters are within their respective
univariate ranges.
Thus, a PLS model can be used to create a fingerprint of the process that detects a larger number of potential shifts, trends and excursions that would not be detected by univariate monitoring tools.”
Quality System: Alert and Action Limits
“For those parameters that are not built into this PLS model, additional monitoring such as univariate SPC charts, and other routine process monitoring will be carried out. Because of its utility as a process monitoring tool, the PLS model will also have alert and action limits; and when the process result exceeds the action limit a deviation will be initiated.”
Quality
System and Planning Supports CPV
Management Review Feedback Loop
Adjust Process Feedback Loop
Avoid Surprise Feedback Loop
Root Cause
Qualification Plan / Schedule
Data Collection and Evaluation
Trending and Calculations
Change Control System
Deviation System
Complaint System
Continued Facility Maintenance
Feedback Loop No overreaction
Acknowledgements
The A-mAb Case Study Team
– Abbott – Amgen – Eli Lilly
– Genentech – GSK
– MedImmune – Pfizer
Back Up
Process: Monoclonal Antibody Production
Thaw:
Working Cell Bank
Harvest- Centrifugation / Depth Filtration
Filtration Viral
Inactivation
Cation Exchange
Capture Protein A
Viral Removal Filtration
Anion Exchange
Filtration Inoculum
Expansion
Seed Bioreactors
Production Bioreactor
Antibodies Produced
Quality Group to Enable the KM Program
GMP
Pharmaceutical Development
Commercial
Manufacturing Discontinuation Technology
Transfer
Investigational products
Management Responsibilities
Process Performance & Product Quality Monitoring System Corrective Action / Preventive Action (CAPA) System
Change Management System Management Review PQS
elements
Knowledge Management Quality Risk Management Enablers