Basis of the Proposed Tactical Plan for a QbD approach for Quality Control and Assurance of Dissolution Rate

1. ACPS Meeting, May 2005 Basis of the Proposed Tactical Plan for a QbD approach for Quality Control and Assurance of Dissolution Rate Ajaz S. Hussain, Ph.D. Deputy Director, Office of Pharmaceutical Science, CDER, FDA

2. Topic #1 ACPS Discussion Goals • Seek ACPS recommendations on a proposed regulatory tactical plan • QbD based regulatory decision system for quality assurance and control of optimal drug dissolution rate over the life cycle of a product • Are the tactical steps outlined consistent with the QbD goals we seek to achieve? • What additional steps and/or changes would you recommend to improve this plan? • What additional scientific evidence is necessary to support the development and implementation of this plan? • General considerations for identifying and developing statistical procedures • Any other specific recommendations? • Prioritization?

3. Proposed Steps • Alternate regulatory approach – suitability of dissolution measurement system • Gauge Reproducibility and Repeatability Study Using Pivotal Clinical or Bio Lot • Systems-based decision tree or establishing dissolution rate specification • Opportunities for utilizing the PAT approach for controlling dissolution rate and development of real time quality assurance strategies • Decision tree for “design space” concept articulated in the draft ICH Q8 • Develop a side-by-side comparison New and Generic Drugs and explain why the level of QA/QC confidence in the proposed approach should be higher than what is achieved under the current system

4. Proposed Steps • Seek ACPS recommendation at the May 2005 meeting on general considerations for identifying and developing statistical procedures • Develop a detailed proposal for a subsequent meeting of the ACPS • Seek harmonization on the approach with other regulatory authorities (starting with ICH Q8 Part 2)

5. What do we intend to accomplish? • Improve our ability to identify sources and types of variability and ensure QbD • Obtain robust estimates for use in regulatory decisions • Regulatory specifications and in-process controls • For assessment of adequacy of proposed material and manufacturing process control strategies • Facilitate assessment and communication of technology/knowledge transfer and assurance of “state of control” in production operations • Provide regulatory flexibility for continuous improvement

6. Inspiration for the proposal …. http://www.ge.com/sixsigma/SixSigma.pdf

7. Quality – Clinical Gap! CMC & CGMP Commitments* CMC – CGMP Gap* “Market Failure”! “Corrective Actions” the only * leverage for continuous improvement Specification – Capability Gap* http://www.ge.com/sixsigma/SixSigma.pdf *Opportunity for continuous improvement* Challenges to overcome! The Pharmaceutical Quality: Challenges and Opportunities

8. Step# 2 Gauge R&R (pivotal clinical or bio lot) Analysis of Variance Apparatus Disso. Media Operator Pivotal Clinical Lot Design of Experiment Pivotal Clinical Lot for GR&R* Considerations Pharmaceutical Development Stability Sampling [Currently Marketed Products?] Tactical Step #1 Measurement System Suitability Alternate Suitability Method Focusing on Mechanical and Media Factors Information collected should facilitate a shift from deterministic to a probabilistic design culture

9. Gauge R&R (pivotal clinical or bio lot) Analysis of Variance Apparatus Disso. Media Operator Pivotal Clinical Lot Design of Experiment Pivotal Clinical Lot for GR&R Considerations Pharmaceutical Development Stability Sampling Currently Marketed Products? Tactical Step #2: Gauge Reproducibility and Repeatability Study Using Pivotal Clinical or Bio Lot Information collected should facilitate a shift from deterministic to a probabilistic design culture

10. Considerations for Decision Trees: Steps # 3-5 • Ask the “right questions” • Begin with end in mind – Intended use • System based (connecting the key disciplines and regulatory submission sections) • Facilitate structured product development process, yet not dictate a specific process • Leverage pre-approval changes & “bridging studies” • Cumulative – and support use prior knowledge • Scientific hypothesis format • For example …….the following several slides are for illustration purposes

11. Quality – Clinical Gap: Uncertainty Leslie Z. Benet, Ph.D. ACPS Meeting April 14, 2004 • “The Current U.S. Procrustean Bioequivalence (BE) Guidelines” • The manufacturer of the test product must show using two one-sided tests that a 90% confidence interval for the ratio of the mean response (usually AUC and Cmax) of its product to that of the reference product is within the limits of 0.8 and 1.25 using log transformed data. • (Procrustean  marked by an arbitrary, often ruthless disregard for individual differences or special circumstances.) • Note: BCS is a non-Procrustean advance • We should consider other non-Procrustean advances “Most discriminating” (Risk Mitigated) Product specifications based on mechanistic understanding of how formulation and process factors impact product performance

12. Additional Challenge: Uncertainty Management Without Pharmaceutical Development Knowledge • Focus on “discriminating” test • Often a “shot gun” approach (e.g., 3-5 different dissolution media focus on pH) • Considered necessary to find the “most discriminating” pH (often could be predicted from physico-chemical properties and formulation design) • In practice, a frequent tendency is to utilize 0.1 N HCl • From “in vivo” relevance perspective • Quality assurance Vs. In vivo relevance debates

13. Mechanistic Understanding? Build on ICHQ6A Concept • For example – “Particle size distribution testing may also be proposed in place of dissolution testing when development studies demonstrate that particle size is the primary factor influencing dissolution; justification should be provided.” • ICHQ6A 3.3.2.3 Parenteral Drug Products • Mechanistic understanding – identification and scientific justification of causal physical or chemical relationships between pharmaceutical materials and/or process factors • Note – establishment of “correlation” between two characteristics may not always be causal

14. Specifications, Standards and Control Limits If, Specification = Standards (no room for risk based decision) • Specification = Standard • Non-conformance rejection or recall • Control limit • Target value • Common cause variability • Alert limit • Potential “Special cause” – investigate, if take necessary action to prevent OOS Control Limit Alert Limit

15. Step # 6: General Considerations for Identifying and Developing Statistical Procedures • Routine production • Control charts of variables (not attributes) • Target value +/- Upper and Lower Limits • Process capability analysis • Not “hypothesis testing” on every lot • Validation, Specification and Standard • Hypothesis testing • Parametric or non parametric tolerance interval • For example: To assure the dissolution quality by controlling the percentage P of the lot with dissolution greater than Q can be set up by testing the following hypotheses • H0: Pr(X  Q)  P vs. Ha: Pr(X  Q) > P • Yi Tsong and Meiyu Shen, Office of Biostatistics, CDER, FDA (FDA Science Forum 2005)

16. Topic #1: Questions to ACPS • Are the tactical steps outlined consistent with the QbD goals we seek to achieve? • What additional steps and/or changes would you recommend to improve this plan? • What additional scientific evidence is necessary to support the development and implementation of this plan? • General considerations for identifying and developing statistical procedures • Any other specific recommendations? • Prioritization?

17. Why have we not used it in our decision process? • The challenge of “destructive test” – i.e., test sample is destroyed • Hesitancy - variability of units in a pivotal clinical batch to be used – assuming or declaring this as “acceptable”? • Organizational gaps – awareness of these issues • A potential paradoxical scenario when dissolution test is the only basis of demonstrating stability of a process

18. Addressing the hesitancy and the potential paradox • Structured development information and robust estimates of variability • For this approach the pivotal clinical trial lot(s) must be “stable” (and capable) and its variation understood to the extent that units may be sampled (e.g., stratified plan) for a “destructive” Gauge R&R study • Contribution to an improved assurance of quality over what we achieve today

19. Is this a stable process? Non-homogeneous Distribution of Magnesium Stearate Ajaz Hussain. Blend Uniformity: Update. 19 July 2001, ACPS Meeting

20. Need to debate Engineering control Vs. Statistical process control? • Preferred State: “Statistical Process Control” • Some processes never reach a state of Shewharts’ statistical control despite heroic efforts. • But, often the average level and the variability of the data are so far inside the specification limits that acceptable product is being made and distributed. Lynn Torbeck. The Sector Chart: A new engineering graph for pharmaceutical processes. Pharmaceutical Technology. April 2005