Process Analytical Technologies (PAT) Sub-Committee Report ACPS Meeting October 21, 2002

1. Process Analytical Technologies (PAT) Sub-Committee Report ACPS Meeting October 21, 2002 Tom Layloff, Ph.D.

2. Meetings • February 25 and 26, 2002 • Applications & Benefits • Process & Analytical Validation • Chemometrics • June12 and 13, 2002 • Product-Process-Development • Process and Analytical Validation • Proposed PAT Certification Program • October 23, 2002

3. Product and Process Development1 • R&D efforts in pilot plant • Process understanding • Identify PATs for manufacturing • Suitability for “intended use” • Demonstrate it is validatable • More rigorous performance requirements if intended to replace end product testing

4. Product and Process Development2 • R&D efforts in manufacturing • Acceptable verification of suitability and validation • Investigate transferability (equipment design and qualification, scale-up issues, safety,…) • Model refinement may be necessary (e.g., “process signature” developed in R&D may be based on limited data) • Submission as a “protocol” in original application or as a prior approval supplement - need to consider less burdensome filing mechanisms

5. Product and Process Development3 • Routine manufacturing using PATs • Body of PAT information should have equivalent or better informing power than the corresponding conventional approved end product test • Recommend setting-up a table, showing relationship between PAT testing and current testing methodology • Parallel PAT testing and conventional testing (in-process and/or release tests) should be performed for sufficient number of batches • Level of redundancy - often a business decision

6. Product and Process Development4 • Identify steps for resolving OOS obs. • PATs may allow selective rejection, or partial batch release • Within batch trend information should facilitate resolution of OOS • Until PATs are approved for regulatory purposes, the approved conventional test results supersede the PAT results • If OOS result is traced to instrument failure, then traditional approved methods can be utilized for batch release in lieu of PAT based results

7. Product and Process Development5 • Identification of relevant critical formulation and process variables • Based on product performance, adequate process control, and assurance of quality • Justification for use of indirect or inferential measurements (e.g., process signature or correlation) • Demonstrate a link (statistical + causal) between PAT parameter and product characteristics • Establish acceptable variability

8. Process &Analytical Validation1 • Definition • Systems for the analysis and control of manufacturing processes based on timely measurement during procession of critical quality parameter and performance attributes of raw and in-process materials and processes, to assure acceptable end-product quality at the completion of the process.

9. Background: Process &Analytical Validation2 • Existing validated measurements invariably correlate poorly with process performance • Univariate measurements used to infer compliance of multivariate dynamic systems • Measure what we can measure, not what needs to be measured • Measurement has not been seen as process related • Measurement needs to respond to process need over the product life cycle • Need to understand the process • Recognize that the conventional approach to validation might be limiting

10. Background: Process &Analytical Validation3 • Understand the Process • Break down into unit operations • Assess risk potential for each unit individually and collectively using techniques, e.g. experimental design • Design systems to manage risk • Univariate measurement • Multivariate systems • Develop systems • Establish proof of concept • Challenge validation • Objective - confirm processes and measurement validity in “real” time across life cycle

11. Process &Analytical Validation4 • Validation Protocols will be different for new products associated with well-designed understood manufacturing processes and existing products where PAT is applied retrospectively • The validation plan will reflect the total system design concept since an Real-Time-QC/QA manufacturing process, statistically based, essentially revalidates itself on every manufacturing batch • The rationale for model validation incorporating pass/fail criteria must be clearly defined thereby establishing the authenticity of predictions in routine manufacturing and ensuring compliance

12. Process &Analytical Validation5 • Three distinct ways of analyzing unit operations and releasing products • Current operating scenario. Product is manufactured according to fixed process conditions set during development and confirmed during initial process and product validation. Release is conducted by physical and chemical testing subsequent to manufacture. • Product is manufactured according to process conditions that have been shown during development and manufacturing to infer product performance and is confirmed during initial process and product validation. Relationships are developed and confirmed with physical and chemical testing subsequent to manufacturing runs. Release is conducted by review of process conditions during each batch manufacture.

13. Process &Analytical Validation6 • Three distinct ways of analyzing unit operations and releasing products • #3. Product is manufactured according to process conditions that are responding to direct measurement of in-process product quality or unit dosage forms as they are being manufactured. Relationships are developed between process and product performance that are optimized and bounded by data obtained in development and manufacturing runs. Release is conducted by data collected from in-process product or each dosage form during manufacture. • Release specification form and validation criteria can be defined for each condition based on the nature of their release.

14. Process &Analytical Validation7 • Recommendations for guidance • Suitable for intended purpose • General validation criteria • References to existing guidance documents (ICH Q2: a and b Analytical Validation, Q6: a and b, Specifications, FDA Analytical Procedures and Methods Validation) • Research exemption • OOT/OOS • Encourage use of PAT. FDA should have a mechanism to institute these new technologies and methods

15. Training & Certification1 • Proposed Process Analytical Technology Certification Program for FDA Investigators and Reviewers • On completion of this certification program, participants should be able to evaluate the adequacy and performance of current and emerging PATs. This certification will require a demonstrated understanding of the fundamentals, importance, and impact of PATs

16. Training & Certification2 • Participants will be able to demonstrate an understanding of: • The distinguishing characteristics of a PAT • The identification and use of PCCPs (process critical control points) • Suitability and validity of the statistics, chemometric, and instrumental approaches applied to PAT • Typical PAT applications and the associated capabilities and limitations of the methodology • Data handling, analytical, control and engineering tools and vocabulary relevant to PAT

17. Meeting #3 (10/23/02) • Discuss issues related to computer software validation, security, electronic batch records, signatures,.. • Breakout session on • “Mock” PAT submissions • Rapid microbial testing • At the end of this meeting information needed to develop a “general” guidance should be available to the FDA

18. PAT: FDA Progress Report Ajaz Hussain, Ph.D.

19. Progress Report • PAT Review - Inspection team assembled • ORA, CDER, and CVM • Successful team-building meeting • Training curriculum developed and contracts established (Univ. Washington (CPAC), Univ. Tennessee (MCEC), and Univ. Purdue) • PAT Policy Development Team • Successful recruitment • PAT Research • Publications and presentations

20. PAT Team: ORA, CDER & CVM PAT Steering Committee Doug Ellsworth, ORA/FDA Dennis Bensley, CVM/FDA Mike Olson, ORA/FDA Joe Famulare, CDER/FDA Yuan-yuan Chiu, CDER/FDA Frank Holcomb, CDER/FDA Moheb Nasr, CDER/FDA Ajaz Hussain Chair, CDER/FDA PAT Review - Inspection Team Investigators: Robert Coleman (ORA/ATL-DO) Rebecca Rodriguez (SJN-DO) Erin McCaffery (NWJ-DO) George Pyramides (PHI-DO) Compliance Officers: Albinus D’Sa (CDER) Mike Gavini (CDER) William Bargo (CVM) Reviewers: Norman Schmuff (CDER) Lorenzo Rocca (CDER) Vibhakar Shah (CDER) Rosario D’Costa (CDER) Raafat Fahmy (CVM) PAT Policy Development Team Raj Uppoor, OPS/CDER Chris Watts, OPS/CDER Hiquan Wu, OPS/CDER PAT Training Coordinators John Simmons, Karen Bernard and Kathy Jordan

21. In-put from ACPS PAT-Subcommittee • Conceptual framework on PAT • Developed, consensus established • PAT as a part of, and a example of, the new FDA wide initiative “cGMP for the 21st Century”

22. Development/Optimization/Continuous Improvement (DOE, Evolutionary optimization, Improved efficiency) Incoming Materials. Specifications Relevant to “Process-ability” LT Control of process critical control points (PCCP). Process end point (PEPs’) range based on “performance” attributes. Multivariate Systems Approach Risk Classification and Mitigation Strategies PAC PAC PCCP PEP’s At-line In/On-Line Process Analytical Chemistry Tools Laboratory or other tests CM IT PAC LT Incoming material attributes used to predict/adjust optimal processing parameters within established bounds (more flexible bounds) Chemometrics (CM) and IT Tools for “real time” control and decisions Direct or inferential assessment of quality and performance (at/on-line) PAT: Conceptual Framework

23. Product and Process Quality Knowledge: Science-Risk Based cGMP’s Quality by Design Process Design Yes, Limited to the Experimental Design Space Maybe, Difficult to Assesses GMP/CMC FOCUS Design qualification Focused; Critical Process Control Points (PAT) Extensive; Every Step (CURRENT) 1st Principles MECHANISTIC UNDERSTANDING CAUSAL LINKS PREDICT PERFORMANCE DECISIONS BASED ON UNIVARIATE APPROACH DATA DERIVED FROM TRIAL-N-ERROR EXPERIMENTATION

24. Low Medium High High Medium Low Quality Risk Classification (based on SUPAC and GAMP-4) Quality by design + Systems approach Risk Likelihood Level 3 Level 2 Impact on Quality Level 1

25. Quality Risk Priority Quality by design + Systems approach Probability of Detection Low Medium High High 3 Medium Risk Classification 2 Low 1