1. May 20, 2014 Using Statistical Innovation to Impact Regulatory Thinking Harry Yang, Ph.D. MedImmune, LLC

2. 204/14/2008 – 6:00pm eSlide - P4815 - MedImmune Template How Do We Influence Regulatory Thinking?

3. 304/14/2008 – 6:00pm eSlide - P4815 - MedImmune Template An Old Tried and True Method  Throw statisticians at the deep end of regulatory interactions

4. 404/14/2008 – 6:00pm eSlide - P4815 - MedImmune Template An Old Tried and True Method (Cont’d)  Throw statisticians at the deep end of regulatory interactions –Low success rate –Lost potential/opportunities

5. 5 eSlide - P4815 - MedImmune Template A More Effective Approach to Influencing Regulatory Thinking  Identify opportunities Understand our own strengths Influence thru collaboration Opportunities

6. Three Case Examples  Acceptable limits of residual host cell DNA  Risk-based pre-filtration limits  Bridging assays as opposed to clinical studies 6

7. Acceptable Residual DNA Limits  Biological product contains residual DNA from host cell  Residual DNA could encode or harbor oncogenes and infectious agents  Mitigate oncogenic and infective risk thru restriction on DNA amount per dose and size  WHO and FDA guidelines recommend –Amount ≤ 10 ng/dose –Size ≤ 200 base pairs (bp) 7

8. Safety Factor  Safety factor (Pedan, et al., 2006) –Number of doses taken to induce an oncogenic or infective event O m : Amount of oncogenes to induce an event I 0 :Number of oncogenes in host genome m i :Oncogene sizes M:Host genome size E[U]:Expected amount of residual hose DNA/dose

9. Revised Safety Factor (Lewis et al., 2001) O m : Amount of oncogenes to induce an event I 0 :Number of oncogenes in host genome m i :Oncogene sizes M:Host genome size E[U]:Expected amount of residual hose DNA/dose P: Percent of DNA with size ≥ oncogene size

10. DNA Inactivation 10

11. Relationship between Enzyme Cutting Efficiency and Median DNA Size (Yang, et al., 2010) 11 Probability of enzyme cutting thru two adjacent nucleotides, p, and median DNA size Med satisfy

12. Safety Factor Based on Probabilistic Modeling (Yang et al., 2010) I 0 :Number of oncogenes in host genome m i :Oncogene sizes M:Host genome size Med 0 :Median residual DNA size E[U]:Expected amount of residual hose DNA/dose

13. Method Comparison  Theoretically it can be shown FDA methods either over- or under- estimate safety factor (Yang, 2013)

14. Risk-based Specifications 14

15. DNA Content and Size Can Be Outside of Regulatory Limits without Compromising Safety! 15

16. Establishing Pre-filtration Bioburden Test Limit 16

17. EMA Guidance (2008): Notes for Guidance on Manufacture of Finished Dosage Form 17

18. EMA Guidance (2008): Notes for Guidance on Manufacture of Finished Dosage Form 18

19. Risk Associated with Three Different Test Schemes 19 20 CFU 32 CFU 63 CFU 5%

20. Mitigating Risk of Larger Number of Bioburden thru Sterial Filtration 20

21. Sterile Filtration 21  FDA guidance requires that filters used for the final filtration should be validated to reproducibly remove microorganisms from a carrier solution containing bioburden of a high concentration of at least 10 7 CFU/cm 2 of effective filter area (EFA)

22. Upper Bound of Probability p 0 for a CFU to Go Thru Sterile Filter (Yang, et al., 2013) 22

23. Upper Bound of Probability of Having at least 1 CFU in Final Filtered Solution  It’s a function of batch size S, pre-filtration test volume V, and the maximum bioburden level D 0 of the pre-filtration solution  By choosing the batch size, this probability can be bounded by a pre-specified small number δ. 23

24. Maximum Batch Sizes Based on Risks and Pre- filtration Test Schemes 24

25. 2504/14/2008 – 6:00pm eSlide - P4815 - MedImmune Template Bridging Assays as Opposed to Clinical Studies  FFA and TCID50 are different assays but both used for clinical trial material release (Yang, et al., 2006) Theoretical mean difference

26. 26 eSlide - P4815 - MedImmune Template Other Ways to Influence Regulatory Thinking  Serve on committees such as USP Statistics Expert, CMC Working Groups, Industry Consortiums  Organize joint meetings/conferences/workshops

27. 27 USP Bioassay Guidelines  Originally USP and EP 5.3  was split into two chapters, USP Design and Development of Biological Assays and USP Analysis of Biological Assays  Biological Assay Validation added to the suite “Roadmap” chapter (to include glossary) 27

28. 28 eSlide - P4815 - MedImmune Template Parallelism Testing  Significance vs. equivalence test (Hauck et al., 2005)  Feasibility of implementation (Yang et al., 2012)  Method comparison based on ROC analysis (Yang and Zhang, 2012)  Bayesian solution (Novick, Yang, and Peterson, 2012)

29. Testing Assay Linearity  Directly testing linearity (Novick and Yang, 2013)  Testing linearity over a pre- specified range (Yang, Novick, and LeBlond, 2014)  The method is being considered to be included in a new USP chapter on statistical tools for method validation 29

30. A Few Additional Thoughts 30

31. 31 eSlide - P4815 - MedImmune Template Conduct Innovative Statistical Research on Regulatory Issues  Solutions based on published methods are more likely accepted by regulatory agencies

32. Take a Good Statistical Lead in Resolving Regulatory Issues 32

33. Regularly Communicate with Regulatory Authorities 33

34. 3404/14/2008 – 6:00pm eSlide - P4815 - MedImmune Template Conduct Joint Training

35. References  H. Yang, S.J. Novick, and D. LeBlond. (2014). Testing linearity over a pre-specified range. Submitted.  H. Yang, N. Li and S. Chang. (2013). A risk-based approach to setting sterile filtration bioburden limits. PDA J. of Pharm. Science and Technology. Vol. 67: 601-609  D. LeBlond, C. Tan and H. Yang (2013). Confirmation of analytical method calibration linearity. May – June Issue, Pharmacopeia Forum. 39(3).  D. LeBlond, C. Tan and H. Yang. (2013). Confirmation of analytical method calibration linearity: practical application. September - October Issue. Pharmacopeia Forum  S. Novick and H. Yang. (2013). Directly testing the linearity assumption for assay validation. Journal of Chemometrics. DOI: 10.1002/cem.2500  H. Yang. Establishing acceptable limits of residual DNA (2013). PDA J. of Pharm. Sci. and Technol., March – April Issue. 67:155-163  S. Novick, H. Yang and J. Peterson. A Bayesian approach to parallelism testing (2012). Statistics in Biopharmaceutical Research. Vol. 4, Issue 4, 357-374.  H. Yang, J. Kim, L. Zhang, R. Strouse, M. Schenerman, and X. Jiang. (2012). Parallelism testing of 4-parameter logistic curves for bioassay. PDA J. of Pharm. Sci. and Technol. May-June Issue, 262-269.  H. Yang and L. Zhang. Evaluations of parallelism test methods using ROC analysis (2012). Statistics in Biopharmaceutical Research. Volume 4, Issue 2, p 162-173  H. Yang, L. Zhang and M. Galinski. (2010). A probabilistic model for risk assessment of residual host cell DNA in biological product. Vaccine 28 3308-3311  H. Yang and I. Cho. (2006) Theoretical Relationship between a Direct and Indirect Potency Assays for Biological Product of Live Virus. Proceedings of 2006 JSM. 35

36. 3604/14/2008 – 6:00pm eSlide - P4815 - MedImmune Template Q&A