QbD in Generics IFPAC 2015 Teva Inna Ben Anat QbDWorks
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The document discusses the implementation of Quality by Design (QbD) within the generic pharmaceutical industry, emphasizing the growing complexity and competition in the market. It outlines the shared responsibility of ensuring drug quality by both the FDA and drug companies, aiming for robust supply of high-quality, affordable medications. Key focus areas include risk assessment, process optimization, and regulatory changes under GDUFA, highlighting the transition toward improved quality submissions and lifecycle management.
QbD in Generics IFPAC 2015 Teva Inna Ben Anat QbDWorks
- 1. QbD implementation in Generic Industry: Are we there yet? Inna Ben-Anat, Director, Global QbD and Product Robustness, Teva Pharmaceuticals Jan 2015
- 2. Generic Industry is functioning at a Rapid Pace
- 3. The Market is very Competitive
- 4. Products Complexity is growing
- 5. The bar for Quality of Applications is raising
- 6. From our perspective: Patients deserve quality medications Lawrence X. Yu, Ph.D., Acting Director of FDA’s Center for Drug Evaluation and Research’s Office of Pharmaceutical Science, discusses the important roles of FDA and drug companies in ensuring quality drug products. Drug quality -- a shared responsibility At t he end of t he day, it ’s t he pat ient s w ho rely on t heir m edicat ions and w ho suffer t he consequences of poor qualit y; Providing pat ient s w it h subst andard or cont am inat ed m edicat ions is sim ply unaccept able. From our perspective: Patients deserve quality medications (Dr. Lawrence X. Yu, Acting Director, OPS, January 5, 2015) http://www.fda.gov/Drugs/NewsEvents/ucm428298.htm
- 7. The ultimate goal-robust supply of highest quality affordable medication for the patient (~ 80% prescriptions in US are generics)
- 8. Quality by Design Enhanced Product and Process Understanding Are We There Yet ???
- 9. Where we are today with QbD Implementation
- 10. Published FDA Case-Studies FDA/GPhA CMC Meeting, May 2013
- 11. Updated QbD Implementation Paper Understanding Pharmaceutical Quality by Design
- 12. Product and Process Understanding
- 13. QbD for Generics: Finding the right balance between Speed, Efficiency and Excellence FTF, FTM Highest Quality & Excellence Efficiency, Resources
- 14. GPhA QbD Workshop, October 2014
- 15. Product Development Outline based on QbD Methodology • Characterization of the Reference Listed Drug (RLD) • Defining Quality Target Product Profile (QTPP) • Identification of Critical Quality Attributes (CQAs) Design/ Definitions • Identification and evaluation of potential risks related to Drug Product Components (DS and Excipients – stability and compatibility), Formulation and Manufacturing Process, etc. Risk Assessment • Screening and optimization of formulation and Critical Material Attributes (CMAs) • Development of a robust process (DOE for high risk Critical Process Parameters, CPPs) Risk Evaluation • Establishment of Control Strategies and manufacture of the exhibit batches • Continuous Monitoring and Improvement Control/ Implementation Prior Knowledge Management, Risk Assessment Techniques, Statistically Designed Experimentation, Data Management are some of the tools that will assure the desired balance between Efficiency and Excellence
- 16. QbD Implementation in Generics: Where we are today QbD format (content?) in almost 100% submitted applications Follow the QbD implementation guideline as illustrated in published IR/MR case-studies (sometimes too close? ) o Include all five QbD Elements in submission (QTPP, CQAs, Product Design, Process Design, Control Strategy) (supported by relevant data? ) Routinely Perform Risk Assessment and utilize Risk Assessment tools (Cause and Effect diagrams, Risk Ranking, FMEA etc.) (proactively?) Conduct Design of Experiments-the ultimate tool to demonstrate enhanced product and process understanding (based on the Risk Assessment? understanding practical significance and interactions? ) Enhanced statistics utilization o Statistically trend and assess Stability Data o Assessing Process Capability, Data-Driven Justification of Specifications (before CRL is asking to justify/tighten?) subconscious
- 17. Efficiency and Excellence: Finding the Balance It is not about the amount of the data, but its relevance… Adequate planning of the right/relevant studies based on proactive risk assessment will assure the balance between efficiency and excellence and Right First Time applications
- 18. The ‘Magic’ of Statistics….. "To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of.“ -- Ronald Fisher (1938) OFAT trials analyzed statistically won't bring as much insight as DOEs, which are carefully designed to protect against noise, bias and confounding and identify interactions Joe, Pharma Statistician Mike, Formulator Joe, can you do your magic with this data we collected from 5 recent OFAT Trials?
- 19. New from the Regulator…. Supporting Regulatory Landscape GDUFA - encourages RFT High Quality Submissions Updated QbR - formalizes QbD milestones and format Shift in Complete Response Letters questions ‘culture’ toward Product and Process Understanding Future ICH Q12 –Lifecycle Management Next
- 20. What does GDUFA mean for the Industry • What worked in the past will not work under GDUFA! Transformational Change! • Increased focus on Quality: Develop robust products; submit high quality ANDA and maintain high quality in manufacturing; Quality will either be a differentiator or a barrier • Requires complete visibility of the suppliers ; APIs, third party manufacturers, testing sites, packaging sites, etc • Higher barrier to entry in the market - building the quality into the systems. • Robust product & process: Implementation of Full Quality by Design • Ensure 100% alignment and training of all R&D and Regulatory staff on new Guidance's and GDUFA requirements. • Further collaboration among the network from product identification through commercialization Strategy Execution back
- 21. QbR, November 2014
- 22. QbR, November 2014 back
- 23. Example of CRL Comment back
- 24. ICH Q12 Final Business Plan back
- 25. How Do We (Industry) Measure Success
- 26. Defining QbD Success: Metrics and KPIs examples Short Term KPIs examples: % QbD Submissions (include 5 elements outlined by FDA) % Submissions contain Formulation/Process DOE-enhanced approach % Submissions utilizing PAT tools-enhanced approach Long Term metrics examples: Success rate of scale-up, validation and launch Overall products quality and robustness improvement Shorter review cycle/fewer DL rounds-RFT Submission
- 27. Where we want to be… How will we get there…
- 28. Areas of Additional Focus PAT Implementation and application More hands on experience with QbD application for additional dosage forms (injectables, patches, films, etc.) Leveraging prior knowledge efficiently: effective knowledge management platform, historical data mining and filtering capabilities Leverage QbD based development throughout product validation and commercialization-lifecycle approach Manage Risk Assessment (QRM) throughout product lifecycle Leverage QbD-gained knowledge through CPV stage Data collection and analysis automation
- 29. QbD doesn’t end once file is submitted It’s a lifecycle approach Implementation of QbD Principles post- submission • Risk Assessment, DOE and Historical Data Mining techniques utilization • Product Robustness initiatives • CPV (Continuous Process Verification)
- 30. Statistical Tools to Support product life-cycle (PDA) https://store.pda.org/ProductCatalog/Product.aspx?ID=2395
- 31. R&D Stage: Formulation/Process optimization, DOE CMAs and CPPs are identified and their impact on CQAs is understood; Product Robustness is designed in and assessed with simulation tools ‘All examples are for illustration purposes only’ Variability explained Factors’ significance RelationshipRelationship
- 32. Submission Stage: Stability Data Assessment, 3 Submission batches Stability Platform assures compliance with ICH requirements and provides comprehensive overview of stability data assessment and batches poolability ‘All examples are for illustration purposes only’
- 33. Submission/Process Design Stage: Uniformity VCA (Variance Components Analysis) VCA platform provides enhanced uniformity and process robustness assessment already at R&D stages ‘All examples are for illustration purposes only’
- 34. Development/Process Design Stage: Simulation Tools to evaluate Process Robustness at pilot scale ‘All examples are for illustration purposes only’ Distribution of the predicted output Predicted OOS rate Estimated Process Variability Estimated Analytical Variability
- 35. Continuous Process Verification (CPV) SPC as main tool for CPV stage and continuous improvement throughout life-cycle ‘All examples are for illustration purposes only’
- 36. Are We There Yet? Slow and Steady Progress … Thank you! Any Questions?