Statistical Innovation for Regulatory Impact

1. Harry Yang, Ph.D. Senior Director, Head of Non-Clinical Biostatistics MedImmune, LLC Using Statistical Innovation to Impact Regulatory Thinking 2014 Nonclinical Biostatistics Conference

2. What Roles Are We Playing in Regulatory Affairs? eSlide - P4815 - MedImmune Template

3. What Roles Are We Playing in Regulatory Affairs? • To think? eSlide - P4815 - MedImmune Template

4. What Roles Are We Playing in Regulatory Affairs? • To rule the world? eSlide - P4815 - MedImmune Template

5. What Roles Are We Playing in Regulatory Affairs? • Or to influence? eSlide - P4815 - MedImmune Template

6. The Answer Is… • TO INFLUENCE! eSlide - P4815 - MedImmune Template

7. How Do We Influence Regulatory Thinking? eSlide - P4815 - MedImmune Template

8. An Old Tried and True Method • Throw statisticians at the deep end of regulatory interactions eSlide - P4815 - MedImmune Template

9. An Old Tried and True Method (Cont’d) • Throw statisticians at the deep end of regulatory interactions • Low success rate • Lost potential/opportunities eSlide - P4815 - MedImmune Template

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

11. Areas Where Statistics Is Value-added • Design of experiment (DOE) eSlide - P4815 - MedImmune Template

12. Statistical Designs • Completely randomized designs • Randomized complete block designs • Split-plot designs • Cross-over designs • Latin square designs • Factorial designs • Analysis of variance designs eSlide - P4815 - MedImmune Template

13. Too Many to Choose eSlide - P4815 - MedImmune Template

14. How to Reduce Variability? eSlide - P4815 - MedImmune Template

15. Should You Use Control? eSlide - P4815 - MedImmune Template

16. Should You Be Blinded? • To reduce evaluator’s bias eSlide - P4815 - MedImmune Template

17. Should You Randomize? eSlide - P4815 - MedImmune Template

18. How to Minimize Chance of False Claim? eSlide - P4815 - MedImmune Template

19. How to Maximize Probability of Success? eSlide - P4815 - MedImmune Template

20. Did You Use the Right Sample Size N? • A small N may miss biologically important effects • A large N wastes animals eSlide - P4815 - MedImmune Template

21.  Facts  Science “A collection of facts is no more a science than a heap of stones is a house.” Henri Poincare (1854 – 1912) eSlide - P4815 - MedImmune Template

22. How To Analyze Data with High Accuracy, Precision and Confidence? eSlide - P4815 - MedImmune Template

23. Which Model to Choose? • Analysis of variance (ANOVA) • Regression analysis • Repeated measurement analysis • Survival analysis • Meta-analysis • Mixed effect modeling • Non-parametric analysis eSlide - P4815 - MedImmune Template

24. Help Overcome Regulatory Hurdles eSlide - P4815 - MedImmune Template

25. Be Bold and Innovative eSlide - P4815 - MedImmune Template

26. Four Case Examples • Widening specification after OOS • Bridging assays as opposed to clinical studies • Acceptable limits of residual host cell DNA • Risk-based pre-filtration bio-burden limits

27. eSlide - P4815 - MedImmune Template

28. Bridging FFA and TCID50 Assays • CRL Question: FFA and TCID50 are different assays but both used for clinical trial material release Theoretical mean difference eSlide - P4815 - MedImmune Template

29. Acceptable Residual DNA Limits: The Problem • The product under evaluation contains a significant amount of residual host cell DNA greater than 500 bp in length. • This may increase the risks of oncogenicity and infectivity of host cell DNA. • Regulatory guidance requires the median size of residual DNA be 200 bp or smaller • Our process can only achieve a median size of 450 bp

30. Anxiety Attack The Scream, by Edvard Munch, 1893

31. Safety Factor • Safety factor (Pedan, et al., 2006) • Number of doses taken to induce an oncogenic or infective event Om: Amount of oncogenes to induce an event I0: Number of oncogenes in host genome m: Average oncogene size M: Host genome size E[U]: Expected amount of residual host DNA/dose

32. Safety Factor per FDA-recommended Method • If cellular DNA contained an active oncogene it would take 11.6billion doses to cause an oncogenic event • If 250million doses of vaccines are used annually, in less than 46.4yearsone oncogenic event may be observed * Oncogenic dose derived from mouse

33. Oncogenic risk is overstated • The denominator includes amount of fragmented oncogene DNA Amount of oncogene DNA in final dose = Amount of unfragmented oncogene DNA in final dose + Amount of fragmented oncogene DNA in final dose

34. DNA Inactivation

35. Enzymatic Degradation Inactivates DNA Benzonase and other ingredients

36. Hope • This finding gives us hope that with median residual DNA size of 450 bp (albeit not quite up to the regulatory bar of 200 bp) perhaps the oncogenicity and infectivity risks are already reduced to an acceptable level.

37. Negotiation with FDA • Standard method overestimates risk • If DNA inactivation step is incorporated in the calculation, the risk might be adequately mitigated

38. Burden of Proof

39. How to Incorporate DNA Inactivation in the Risk Assessment? Enzymatic degradation of DNA Source: http://1.bp.blogspot.com/_vgEA7CHGLe8/SzIAZHWs-vI/AAAAAAAAAVc/vZcmDlRlxSY/s320/miracle.gif

40. DNA Inactivation

41. Model of DNA Inactivation Process

42. Safety Factor Based on Probabilistic Modeling (Yang et al., 2010) • Safety factor Amount of oncogenes required for inducing an oncogenic event Expected amount of unfragmented oncogenes in a dose

43. Proof of the Theoretical Result • Trust me!

44. How to estimate enzyme cutting efficiency p?

45. Modeling Length of DNA Segment • After enzyme digestion, any DNA segment takes the form • Let p denote the probability for enzyme to cleave bond c. Thus X has properties • Represents number of trials until the first cut • Follows a geometric distribution with parameter p, • Prob[X=k]=(1-p)k-1p • Median = Length X, random variable

46. Safety Factor • If cellular DNA contained an active oncogene it would take 234 billion doses to deliver the oncogenic dose used in the mouse studies • If 250 million doses of vaccines are used annually, it will take approximately 883 years for one oncogenic event to occur

47. Oncogenic Risk Comparison • FDA method overestimates oncogenic risk by 19-fold • Reducing residual DNA with median size of 450 bp is adequate to mitigate oncogenic risk Our Method FDA Method

48. Establishing Pre-filtration Bioburden Test Limit

49. Manufacture of a Sterile Drug Product • Microbial control during manufacturing is critical for ensuring product quality and safety. • Sterile biologic drug products (finished dosage forms) are typically manufactured by sterile filtration followed by aseptic filling and processing. • Control of microbial load at the sterile filtration step is an essential and required component of the overall microbial control strategy.

50. Measures to Mitigate Bioburden Risk • Pre-filtration testing • Filtration • Minimization of manufacturing hold times between process steps • Utilization of refrigerated storage for intermediates eSlide - P4815 - MedImmune Template