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Managing Pharmaceutical Quality: Risk or Uncertainty Management?

Managing Pharmaceutical Quality: Risk or Uncertainty Management?

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Managing Pharmaceutical Quality: Risk or Uncertainty Management?

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  1. Managing Pharmaceutical Quality: Risk or Uncertainty Management? Ajaz S. Hussain, Ph.D. Office of Pharmaceutical Science CDER, FDA PQRI Workshop February 1, 2005

  2. What is Quality? • What is pharmaceutical quality? • consistent delivery of the label performance and lack of contamination. • operationalzed via a set of pre-specified quality attributes (e.g., specifications, limits) and through the CGMP regulations. • FDA, in its quality definition, is standing in for the customer—and it is apparent that health care practitioners and patients highly value an additional drug attribute: product availability • Good pharmaceutical quality represents an acceptably low risk of failing to achieve the desired clinical attributes.

  3. Management Goals • Improving quality and ensuring availability • Optimal use of our resources • A systems approach to CMC review and CGMP investigations • Based on knowledge and process understanding • Achieving “quality by design” • Demonstrating “science of design” • Continuous learning and improvement through “manufacturing science”

  4. An Approach for Quality – Risk Connection • Concept of Quality by Design (QbD) • Product and process performance characteristics are scientifically designed to meet specific objectives, not merely empirically derived from performance of test batches • Characteristics important to desired performance must be derived from a combination of prior knowledge and experimental assessment during product development. • From this knowledge and data, a multivariate model linking product and process measurements and desired attributes may be constructed. • Clinical study would then be viewed as confirmatory performance testing of the model. Woodcock, 2004

  5. A Systems Approach CMC Reveiw CGMP Investigations Deliver Quality by Design Science of Design Manufacturing Science State of Control & Continuous Improvement

  6. Quality can not be tested into a product; it has to be by design “Market Standards” Science of Design + Manufacturing Science = Quality by Design

  7. Label Acceptable Risk/Benefit No benefit (placebo effect) Harm Quality Risk/Benefit and Quality

  8. Managing Pharmaceutical Quality • Quality of a new molecular entity (a potential drug) • Intrinsic pharmacological & toxicological attributes • Identity • Complexity • A range of uncertainty with respect to identity of “active moiety”, purity and stability of materials used in evaluation of pharmacological and toxicological attributes (if a mixture; variability adds additional uncertainty) • Variability in the extent and rate of delivery of “active moiety” to the sites of action and variability in the pharmacological & toxicological response and measurement systems further adds uncertainty

  9. Managing Pharmaceutical Quality • Quality of a drug product • For establishing proposed therapeutic claim (label) • Drug product manufactured for clinical trials • After successful demonstration of therapeutic claim (acceptable risk-to-benefit ratio) • Drug product manufactured for commercial distribution • Life cycle of the product (shelf-life, exclusivity period, generic competition, post-approval changes,…) • Drug product manufactured at many different facilities, changes in the process, different manufactures,…

  10. Uncertainty, Variability and Risk • Quality – Clinical Connection • How does a product formulation and its manufacturing process impact clinical performance? • Without a clear understanding we are uncertain (lack of knowledge) • In decision making there are many advantages in distinguishing between uncertainty, variability (random variation) and risk

  11. Goals and Characteristics of a Quality Decision System: Example

  12. ANDA Applications: Limited Information Content (e.g., IR Capsule) • Generally 1bio-batch • Bioequivalence goal post 80-125% • 90 % Confidence Interval for the Test/Reference ratio for Cmax and AUC in between the goal post • Normal healthy subjects, cross-over design, fasting (and fed) conditions • Common for all oral drugs – i.e., procrustean • To cover “worst case” scenarios • If mean is 100% and 90% CI is outside 80- 125 say 85 - 126.5? • Executed batch record and master batch record (e.g., 10X) – application commitment • Post-approval process validation and stability commitment • Post approval changes based on SUPAC-IR

  13. Demonstration of “quality by design”? • Analytical data + Executed batch record + bio-study + process validation • IQ, OQ, PQ,.. • PQ = 3 consecutive batches in conformance • Reduced testing – e.g., compendial tests • For simple, conventional product designs works fine most of the time; quality by design is then the prior knowledge and what ever development data is generated (held at site)

  14. Uncertainty, Variability and Risk Uncertainty? Variability? Risk?

  15. Uncertainty, Variability and Risk • Procrustean standards have to address “worst case” scenarios • Uncertainty is not risk, currently we have no choice but to force this equality • Uncertainty is reduced by improving knowledge • We learn what to control and the degree of control necessary to minimize risk • For continuous quality improvement we should focus on improving uncertainty management process

  16. Example of a CMC Regulatory Decision: Acceptability of a Post Approval Manufacturing Process Change • Original NDA or ANDA = CMC Quality & Performance (“Insurance”) Contract • For example in ANDA’s Regulatory commitments = Conditions in executed batch records • Prior Approval Supplement* (PAS) • Product conforms with all established specifications • But - “Specifications do not tell the whole story” • E.g., Shelf-life and/or bioavailability may have changed and/or a new impurity may be introduced that may not be detected with established analytical methods,…sponsor may not adequately qualify changes (inspection frequency may not be sufficient),…. *prior approval supplement for process optimization and continuous improvement efforts

  17. Company X “Goes Lean” • “Cycle-time reduction subgroup members, for example, examine each process function, for example, dispensing, roller compaction and compression, to determine how to speed up changeover and get equipment to run faster and more efficiently.” • “The team solicits ideas at regular meetings and via email. The ideas are then rated from 1 to 10 based on "bang for the buck" to reduce cycle time, and on how difficult they would be to achieve--e.g., whether they will require validation or prior FDA approval.”

  18. Post Approval Process Change (SUPAC Guidance) “Within” (Change Target setting) “Outside”

  19. Current Uncertainty Management • At the operational level the most efficient approach for managing uncertainty is “demand management” • Strict “checking the box” process using pre-specified requirements (recommendations) and procrustean standards • FDA guidance documents, 483 observations,.. • 90% CI 80-125%, in-process blend uniformity tests, ….. SOP’s,…..

  20. Current Demand Management: Characteristics • For conventional products and manufacturing processes - easy to implement, supervise, and mange • Decision responsibility is deferred to a set of “procrustean” standards - liability distributed to the entire pharmaceutical community (e.g., via USP, AAPS, etc.) • For innovative and/or complex products and processes no one is willing to take responsibility for decisions (e.g., develop guidance document) – decision liability is then on the person willing to take a decision.

  21. Current Demand Management: Characteristics • Innovation and continuous improvement slows down and inefficiency increases • The level of quality assurance achieved is difficult to measure and is buried in historical mindset and clinical variability • With increasing complexity a major failure is necessary to signal inadequacies of the system – such a failure is often the only approach to introduce new regulations or improved decision criteria • Challenge to and alternate approaches to current procrustean standards difficult to prove and debates drain resources Without Continuous (Community) Learning: Demand Management is “static” until a crisis is created, it then reacts to replace a current procrustean standard with another.

  22. Continuous Improvement: Enhancing Customer Satisfaction - Reducing Variability

  23. Stable & Capable “Special Cause” or “Common Cause” Corrective Actions Eliminate “Special Cause” Reduce “Common Cause” Variability On the Continuous Improvement Path Minor, Occasional OOS Frequent, Major OOS Unstable Stable- Yes; Capable? State of Control

  24. Improving Uncertainty Management • Demand management • Specified and procrustean standards • E.g., 90% CI 80-125%, in-process blend uniformity tests, ….. SOP’s,….. • Passive management • Quality by Design, demonstrated “robustness” • Can we bring a systems (CMC review and CGMP investigation) perspective to better recognize a company’s ability to achieve quality by design and reduce the need for prior approval supplements? • Active management • Continuous learning and leveraging knowledge to create flexibility • Move towards a risk-based approach • Continuous improvement (quality and productivity)

  25. Opportunities • PAT Guidance • PAT provides the pharmaceutical context for Lean, Six Sigma! • CPG 7132c.08 • Comparability Protocol • Quality Systems Approach to Pharmaceutical CGMP’s • ICH Q8, (9?), (10?)

  26. PAT Guidance • Opens the door to realize the benefits of connecting • Fisher –to- Shewart –to- Deming • Focus on process understanding leading to control of process end-point! • Research data

  27. Drug Substance or API Intended Use Route of administration Patient population ….. Product Design P2.1 and 2.6 Components of drug product P2.2, 2.4, 2.5, 2.6 Drug Product Container Closure System Microbiological Attributes Compatibility (e.g., recon) Design Specifications (Customer requirements) P2.3 Manufacturing Process Development Manufacturing Process CTD-P2 Sec. QbD

  28. Assessment Based on ICH Q8 Information/Knowledge Quality System Risk Classification Process Design & Control Specifications Product Design Intended Use Reliability To Deliver Design Requirements Design Requirements “Design Space” = f (Intended Use * Design * Control)

  29. John C Berridge, Q8 Rapporteur (EFPIA). FDA Manufacturing Subcommittee, July 2004

  30. Knowledge Based Decisions: Require Scientific Generalizeable Knowledge – the “SUPAC GAP” Limited information in NDA/ANDA SUPAC Change Levels based on prior knowledge from the pharmaceutical community (AAPS SUPAC Workshops) + Research; Yet difficult to generalize because of multi factorial aspects + lot of subjectivity Gap = Uncertainty Prior knowledge within a company and a move towards mechanistic Understanding (ICH Q8 is intended to fill this gap)

  31. Uncertainty Management: QbD & Flexibility

  32. Science of Design • Often design and development activities are carried out based on experiential knowledge, intuition and rough guidelines – difficult to communicate to individuals from different backgrounds (the “art” argument) • To learn how to represent designs at a much higher level than the current descriptive “recipe” format (e.g., executed batch records, SOP’s) while rigorously documenting key constrains

  33. A Validated System • We have begun updating our current thinking on validation • Process Validation Requirements for Drug Products and Active Pharmaceutical Ingredients Subject to Pre-Market Approval (CPG 7132c.08, Sec 490.100).  • Rational experimental design and ongoing evaluation of data  • Achieving and maintaining a state of control for a process begins at the process development phase and continues throughout the commercial phase of a product's life-cycle • Risk-based approaches - inspectional scrutiny; use of advanced technologies, and the role of conformance batches in the product life-cycle.  • A focus on three full-scale production batches would fail to recognize the complete story on validation.

  34. Production S y s t e m Facilities & Equipment S y s t e m Quality System Packaging & L a b e l i n g S y s t e m M a t e r i a l s S y s t e m Laboratory Controls System Engineering a Quality System Traditional goals Non-traditional goals (risk based, flexibility, robustness, scalability, continuous improvement, innovation, efficiency,….) Characteristics Complexity, uncertainty Relationships (between goals & characteristics) Knowledge and information centric relationships Fundamental issues Draft Guidance for Industry Quality Systems Approach to Pharmaceutical Current Good Manufacturing Practice Regulations

  35. “Change Control” to “Continuous Improvement” Manufacturing & Quality Assurance Innovation & Continuous Improvement Options PAT - ICH Q8 “Design Space” Development Managed under The Company’s Quality System; Subject to CGMP Inspections (no-change or variation) “Fisher” -“Shewart” -“Deming” Theory of experimental design Statistical Process Control Theory of Variation Maintain “State of Control”

  36. Reactive (examples) Testing to document quality Repeating deviation and out of specification investigations Waiting for FDA guidance to submit ANDA demonstrating therapeutic equivalence of generic products Potential for multiple NDA CMC review cycles Waiting for FDA to approve a prior approval supplement for process optimization and continuous improvement efforts Fear, apprehension Proactive (examples) Quality by design and real time process controls to achieve real time release” Right First Time Innovative approaches for demonstrating therapeutic equivalence of generics Single NDA CMC review cycle Process optimization and continuous improvement efforts within a facilities quality system Ability to utilize prior knowledge Empowerment, recognition By Improving Uncertainty Management we have began a process of engineering a proactive decisions system for pharmaceutical quality