A Late-Stage Monoclonal Antibody in the FDA QbD Pilot Program: Moving from Concepts to Implementation

Transcription

1 A Late-Stage Monoclonal Antibody in the FDA QbD Pilot Program: Moving from Concepts to Implementation Ron Taticek, Ph.D., Director Pharma Technical Regulatory Genentech, Inc. South San Francisco, CA WCBP 2010, Washington, DC January 27, 2010

2 Overview Slide 2 Introduction Background on the Pilot Program Background on MAb1 Schedule of FDA Meetings Meeting No. 1 & Feedback Meeting No. 2 & Feedback Other Health Authority Interactions Lessons Learned Challenges

3 Introduction Slide 3 Roche and Genentech s activities in QbD have both internal and external components: Steering Committee with multiple teams working on implementing QbD for Small Molecules, Biologics and Drug Conjugates (partially) Member of the CMC Bio Working Group that wrote the A- MAb Case Study Member of EFPIA Mockestuzumab Team writing mock S2 filing Participating on ISPE PQLI Part of the FDA Pilot Program Interactions with ex-us Health Authorities (EMA, Health Canada and potentially others) on QbD Attendance/Presentation at Key Conferences

4 QbD Pilot Program for Biotech Products Slide 4 Successful FDA QbD Pilot Program for Small Molecules Biotech program announced on 7/2/08 Was to close on 9/30/09; extended to 9/30/10 Both original and post-approval applications Limited to 10 supplements and 8 original applications Sponsor gets access to OBP QbD thought leaders and product reviewers to solicit input on approach and strategy Genentech has 2 applications in the program: Drug Substance Site Transfer Expanded Change Protocol for Commercial Products (supplement) Original Application (product currently in Late Phase III)

5 Background on MAb1 Slide 5 recombinant humanized IgG1 expressed in CHO cells uses Genentech s antibody platform manufacturing process second generation molecule mechanism of action is B-cell depletion being jointly developed by Roche, Genentech and Biogen-Idec for 3 separate indications simultaneous BLA/MAA filing planned for late 2010; ROW filings shortly thereafter Roche & Genentech s first large molecule QbD filing MAb1 Humanized IgG 1

6 QbD Lifecycle Map Slide 6 Meeting No. 1 Risk Ranking & Filtering or EDI vs. ADI For CQA Identification Risk Ranking & Filtering Meeting No. 4 Process Development Platform Knowledge Quality Attributes Potential CQAs (Ph I-III) Final CQAs (BLA) Control Strategy Robustness Assessment Product Understanding Scientific Literature Process Parameters Design of Process Characterization Studies Process Characterization & Linkage Studies Overall Design Space & CPP Identification (BLA) Lifecycle Management of Design Space Meeting No. 2 (viral clearance) Risk Ranking & Filtering for PC Study Design Meeting No. 3 Risk Assessment (TBD)

7 Completed Program Meetings for MAb1 Slide 7 Meeting Topics CQAs and Design of Characterization Studies for Drug Substance Risk Ranking & Filtering Tool for Product Variants & Process Impurities with MAb1 examples Toxicology Assessment for Raw Materials with examples Risk Ranking & Filtering Tool for Design of Process Characterization Studies applied to various MAb1 unit operations Characterization/Validation Studies for Viral Clearance Approach to incorporate viral clearance into overall Process Design Space Risk Ranking & Filtering Tool for Design of Process Characterization Studies applied to viral clearance step Meeting Date September 15, 2009 December 10, 2009

8 Future Program Meetings for MAb1 Slide 8 Meeting Topics Design Space and CPPs Setting acceptance criteria for CQAs Linking steps across the process Definition of an overall process Design Space Identification of CPPs Control Strategy & BLA Content Overall Control Strategy Specific examples of submission content Quality Overall Summary content Product lifecycle management plan Expanded change protocols Meeting Date February 25, 2010 (scheduled) Early May 2010 (proposed)

9 Meeting No. 1 Slide 9 Meeting No. 1 Risk Ranking & Filtering or EDI vs. ADI For CQA Identification Risk Ranking & Filtering Process Development Platform Knowledge Quality Attributes Potential CQAs (Ph I-III) Final CQAs (BLA) Control Strategy Robustness Assessment Product Understanding Scientific Literature Process Parameters Design of Process Characterization Studies Process Characterization & Linkage Studies Overall Design Space & CPP Identification (BLA) Lifecycle Management of Design Space Risk Ranking & Filtering for PC Study Design Risk Assessment (TBD)

10 Approach to Critical Quality Attributes (CQAs) Slide 10 Category of Attribute Product Variants Process-Related Impurities Composition and Strength Adventitious Agents Raw Materials Assessment Risk Ranking and Filtering Risk Ranking and Filtering Statutory CQA Statutory CQA Compare worst case EDI to ADI Rationale Impact to patient safety and product efficacy is specific to variant in question, MOA, etc. Clinical data from similar products can be used to assess safety Potentially high impact to safety and efficacy Potentially high impact to safety Extensive data available from safety and toxicity studies ADI = Acceptable Daily Intake EDI = Estimated Daily Intake

11 CQA Risk Ranking & Filtering Slide 11 Risk = Impact (2-20) x Uncertainty (1-7) Risk that an attribute impacts safety or efficacy Does the attribute impact safety or efficacy? Determined by the available knowledge. More severe impact = higher value. How confident are we in assigning impact? Determined by the relevance of knowledge. Higher uncertainty = higher value. Safety Bioactivity Literature Clinical PK Lab Immunogenicity Nonclinical

12 Assessment of Raw Materials Slide 12 EDI > ADI? Estimated Daily Intake (EDI) based on: Assumes lowest product titer, no clearance, accumulation of components, highest dose Highest possible patient exposure to raw material from a single dose Acceptable Daily Intake (ADI) based on: Toxicological assessment of the potential health-based risk associated with cell culture & purification raw materials Includes multipliers for route of administration, species barriers, experimental variability, etc.

13 Use of ADI and EDI for Raw Materials Slide 13 Is EDI No clearance < ADI? Yes No testing or clearance studies are needed No Test in-process pools (If assay sensitivity is limiting, perform spiking studies) Is EDI With clearance < ADI? Yes No impact on Control Strategy No Define Control Strategy: Control strategy would factor in clinical experience, process capability/robustness, and testing options ADI = Acceptable Daily Intake EDI = Estimated Daily Intake

14 Discussion from Meeting No. 1: CQAs Slide 14 Discussed the RRF Scoring, the CQA threshold and specifically that some of the cases in the matrices are not actually possible based on the way the scoring has been set up Decision was to update the RRF tool with additional sciencebased constraints a risk assessment tool is not a substitute for good science EDI/ADI assessment does not consider interactions of raw materials with the product itself (e.g., proteolysis activator) Level of detail of information in the BLA will need to sufficient to allow FDA to review and concur with methods used to derive ADI values (for raw materials) and with data used to support criticality assessment

15 Risk Ranking & Filtering Tool to Design Studies Slide 15 Main Effect x Interaction Effect = Risk Score Direct impact to output (CQA, non-cqa or process attribute) Impact of potential interactions with other process parameters on output (CQA, non-cqa or process attribute) Experimental Strategy (multivariate, univariate or none) Impact on Process Attribute or non-cqa (1, 2 & 4) is weighted less than a CQA (1, 4 & 8) Impact is assessed based on likely Design Space ranges Limited/No data result in default to major impact Risk Score to Define Experimental Strategy

16 Discussion from Meeting No. 1: PC/PV Slide 16 BLA will need to include justification of parameter scoring (i.e., platform knowledge, prior knowledge, literature data) Scientific literature is used to inform the risk ranking, but needs to be assessed for applicability to the innovator s process and product (sponsor s data takes precedence over scientific literature) Use and qualification of small scale models Raw material variation being incorporated in the characterization studies

17 QbD Lifecycle Map Slide 17 Risk Ranking & Filtering or EDI vs. ADI For CQA Identification Risk Ranking & Filtering Process Development Platform Knowledge Quality Attributes Potential CQAs (Ph I-III) Final CQAs (BLA) Control Strategy Robustness Assessment Product Understanding Scientific Literature Process Parameters Design of Process Characterization Studies Process Characterization & Linkage Studies Overall Design Space & CPP Identification (BLA) Lifecycle Management of Design Space Meeting No. 2 (viral clearance) Risk Ranking & Filtering for PC Study Design Risk Assessment (TBD)

18 QbD Approach for Viral Clearance Slide 18 Viral clearance is a Critical Quality Attribute Design Space is defined by the most constraining CQA output Viral clearance data used to define design space is based on a combination of two approaches: Historical Approach (per ICH Q5A) + QbD Studies (Multivariate data or studies) Multivariate studies will be designed using same RRF tool (based on product specific and modular validation data, and in-house R&D and literature data) Unique challenges exist to performing large DOE multivariate viral clearance studies Approaches to address the challenges: Leverage extensive in house data and scientific knowledge Use novel approaches for viral clearance

19 Discussion from Meeting No. 2: Viral Clearance Slide 19 One set of acceptable ranges for steps that are claimed to clear virus, i.e., viral clearance Design Space = Design Space BLA will need to include justification of approach, including use of data from other molecules Justification of scoring for main effects and interaction effects will need to be provided in the BLA Rationale for the choice of a particular model Linkage across unit operations will be considered when establishing acceptable ranges for individual steps (steps will be constrained to achieve overall acceptable viral clearance target and other CQAs) Multivariate data generated for one mab may be applied to future mabs to streamline product-specific studies: Stringent criteria to apply platform data Always have product-specific verification

20 Feedback from Meeting No. 2: CPPs Slide 20 For steps without any CPPs, how are upper and lower limits for non-cpps established and defined in the design space? Provide overview of the knowledge base to support statements of those parameters considered critical for each unit operation.

21 Interactions with Other Health Authorities Slide 21 EMA BWP/PAT Team Meeting (September 16, 2009) Health Canada BGTD Meeting (November 23, 2009)

22 Lessons Learned Slide 22 Regulators are very open to providing feedback in the area of QbD and on a work in progress Thinking both at our company and at the Health Authorities is evolving as QbD becomes more real and concrete Overall feedback on the approach is very similar between Health Authorities: agree with the approach in principle, but the proof will be in the submission content

23 Challenges Slide 23 Significantly increased workload on both sides (coordinating numerous additional meetings, writing of briefing packages and preparation of presentations) Establishing a consistent cross-functional position and/or approach to QbD can be challenging Not able to provide actual submission content in briefing packages, as the submission hasn t been written yet Associated with a development project with unexpected road blocks and schedule changes Large Health Authority meetings with numerous attendees on both sides

24 Acknowledgements Slide 24 US Greg Blank Qi Chen Mary Cromwell Reed Harris Kathy Hsia Yung-Hsiang Kao Brian Kelley Lynne Krummen Mike Laird Paul Motchnik Wassim Nashabeh Cristina Sanchez Melody Schmidt Mary Sliwkowski Vassia Tegoulia EU Evagoras Evagorou Bernd Hilger Christoph Luedin Nathalie Yanze Canada Nabil Heinen Linda Jack Sal Kumar FDA OBP EMA BWP/PAT Team Health Canada