Demonstrating a High Degree of Assurance in Stage 2 of the Process Validation Lifecycle

Transcription

1 Process Performance Qualification Demonstrating a High Degree of Assurance in Stage 2 of the Process Validation Lifecycle Wendy Zwolenski Lambert, CQM/OE, RAC CMC Strategy Forum January 28, 2013

2 aligns process validation activities with a product lifecycle concept FDA Guidance for Industry Process Validation: General Principles and Practices, January 2011

3 A LIFECYCLE Approach to Process Validation? All phases in the life of a product from the initial iti development through marketing until the product s discontinuation. ICH Q8(R2) The Validation Group Management

4 Each h manufacturer should judge whether h it has gained sufficient understanding d to provide a high degree of assurance in its manufacturing process to justify commercial distribution of the product. FDA Guidance for Industry Process Validation: i General Principles i and Practices, January 2011 Medical Devices: Context for a High Degree of Assurance Process Development: What We ve Always Done (With Enhancements). Process Performance Qualification: Preparation for the Unexpected Continued Process Verification Quality Planning for Continued Assurance The views expressed are solely those of the presenter

5 High Degree of Assurance in Medical Devices Global l Harmonization Task kforce Quality Management Systems Process Validation Guidance Referenced: US FDA Guidance for Industry Process Validation: General Principles and Practices January 2011 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 The product should be designed robustly enough to withstand variations in the manufacturing process process should ldbe capable and stable tbl to assure continued safe products that perform adequately Outlines statistical approaches to consider for achieving confidence.

6 Process and Product: Design Capability Control BinaJect and DuoDote are registered trademarks of Meridian Medical Technologies, Inc., a Pfizer company. Copyright 2012 Meridian Medical Technologies, Inc., a Pfizer company. All rights reserved. DUO Nov 2012 BinaJect and DuoDote are registered trademarks of Meridian Medical Technologies, Inc., a Pfizer company. Copyright 2012 Meridian Medical Technologies, Inc., a Pfizer company. All rights reserved. DUO Nov 2012 Quality cannot be adequately assured merely by in process and finished i product inspection or testing. FDA Guidance for Industry Process Validation: General Principles and Practices, January 2011

7 Process Capability and Process Qualification Theoretical, Simplified Example Component produced by injection molding 12 cavity mold (each cavity = 1 part) 120 second cycle Injection cycle parameters: Temperature Injection Speed Pressure Cycle Time Cycle Validation (PQ) X 3: High, Midpoint, Low 12 parts X cycles X 60 minutes = 360 parts cycle minute hour hour 2880 parts per 8 hour shift! Processes yield sufficient numbers of samples to apply traditional statistical methods.

8 A High Degree of Assurance Medical Devices Design and Development Controls Process Validation (IQ, OQ, PQ) Monitor and Control / Revalidation Biopharmaceuticals Development Process Qualification Continued Process Verification EngineeringFocus: Adequate component sample sizes = Heavy reliance on statistical methods. LifeScience Focus: Biological systems, few data = Statistics alone may be impractical.

9 High Degree of Assurance at End of Stage 2 Stage 1 Development Stage 2 Process Qualification Stage 3 Continued Process Verification 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. FDA Guidance for Industry Process Validation: General Principles and Practices, January 2011 Which and how much data can be used in conjunction with PPQ data to provide a high degree of assurance? How much commercial scale data is needed? Established platform manufacturing Less? Contract manufacturing organizations More? Can the quality system support an ongoing state of control? Has process and product knowledge been integrated into the system?

10 Development: An Enhanced Approach The complexity of the molecule and manufacturing processes have necessitated enhanced approaches to development Process Development and Characterization ICH Q8 Cell Line Qualification ICH Q5A, Q5B, Q5D Clinical Manufacturing ICH Q7 Analytical Characterization ICH Q6B Stability Testing ICH Q5C Comparability ICH Q5E Risk and Criticality Assessments ICH Q9

11 It s All About Control Strategy Specifications / Release testing Clinical Justification most important Criticality, process capability and detectability Analysisand 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 UNKNOWN Derived from: S. Kozlowski, P Swann / Advanced Drug Delivery Reviews 58 (2006)

12 Communicating a High Degree of Assurance Standardized Terminology Knowledge Management Quality Planning

13 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

14 A mab Terminology The terms attribute(s) and parameters(s) are not categorized with respect to criticality With a lifecycle approach to process validation theperception ofcriticality as a continuum rather than a binary state is more useful. FDA Guidance for Industry Process Validation: General Principles and Practices, January 2011

15 A mab: Criticality Continuum High Criticality Quality Attributes 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 Avoids non critical terminology; may suggest uncontrolled. Low Criticality Quality Attributes

16 Standardized Quality Attribute Terminology Quality Attribute: A molecular or product characteristic that is selected for its ability to help indicate the quality of the product ICH Q5E Quality Attributes Critical Quality Attributes No Nons 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 Product Related Substances: Molecular variants of the desired product 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 d impurities. i i ICH Q6B

17 A Mab Process Parameter Classification Reproduced/Derived from A-mAb Case study

18 Perspective on Key Process Parameters Process performance monitoring: Mi Maintaining ii 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 Key process parameters donot Key process parameters do not affect critical quality attributes.

19 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 parameter criticality is linked to the parameter s effect on critical p y p quality attribute. It is based on the probability of occurrence and detectability and therefore can change as a result of risk management.

20 Standardized Process Parameter Terminology Functional Relationships and Parameter Classification Critical Process Parameters Critical Quality Attributes Key Process Parameters Process Performance Attributes Non Key Parameters Low Risk of Impact

21 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. FDA Guidance for Industry Process Validation: General Principles and Practices, January 2011

22 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 regulatory submissions is more likely Ensure knowledge integration into the quality system (ICH Q10)

23 Documentation and Knowledge Management Process Development Product Characterization Pilot Scale Production Robustness Studies Risk Assessment Lifecycle Document Development Reports Analytical Reports Batch Records Qualification Reports FMEA Report Technical Summary

24 PPQ Protocols and Reports: Re think the Template 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 Rf Reference appropriate stage 1 data and discuss relevance. PPQ Acceptance Criteria How established and why TELL THE WHOLE STORY / MAKE NO ASSUMPTIONS

25 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 samplingto 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.

26 Unexpected Results in PPQ Production Chromatography Operations Drug Substance Feed Rate / Volume increased after 1 st PPQ run to increase titer. What next? Bioreactor Recovery Recovery AEX Recovery Acidic Oxidation Aggregate g Volume Titre Capture (90-100) CEX Variants (3-10) <4% ( ) (70-100) (90-100) (25-35) Process Performance Attributes Quality Attribute Critical Quality Attributes Pilot * % Pilot % Pilot % Pilot % Pilot % Eng PPQ % PPQ % PPQ %

27 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.

28 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.

29 Quality System: Alert and Action Limits For those parameters that are not built into this PLS model 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.

30 Quality System and Planning Supports CPV Data Collection and Evaluation Monitoring and Trending Change Control System Deviation System Complaint System Continued Facility Maintenance Management Review Feedback Loop Adjust Process Feedback Loop Avoid Surprise Feedback Loop Root Cause Feedback Loop No overreaction Qualification Plan / Schedule

31 Thank You Acknowledgements The A mab Case Study Team Abbott Amgen Eli Lilly Genentech GSK MedImmune Pfizer