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

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

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

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

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

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

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

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

9. 9 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 26

27. 2704/14/2008 – 6:00pm eSlide - P4815 - MedImmune Template

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

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 O m : Amount of oncogenes to induce an event I 0 :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 O m (ng)9400* OS1950 GS2.41E+09 I0I0 1 hcDNA (ng)1 Safety Factor1.16E+10 * Oncogenic dose derived from mouse  If cellular DNA contained an active oncogene it would take 11.6 billion doses to cause an oncogenic event –If 250 million doses of vaccines are used annually, in less than 46.4 years one oncogenic event may be observed

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? Source: http://1.bp.blogspot.com/_vgEA7CHGLe8/SzIAZHWs-vI/AAAAAAAAAVc/vZcmDlRlxSY/s320/miracle.gifhttp://1.bp.blogspot.com/_vgEA7CHGLe8/SzIAZHWs-vI/AAAAAAAAAVc/vZcmDlRlxSY/s320/miracle.gif Enzymatic degradation of DNA

40. DNA Inactivation 40

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-1 p Median = Length X, random variable

46. Safety Factor O m (ng)9400 Oncogene size1950 MDCK genome size2.41E+09 Median450 hcDNA (ng)1 Safety Factor2.34E+11  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 O m (ng)9400 Oncogene size1950 MDCK genome size2.41E+09 Median450 hcDNA (ng)1 Safety Factor2.34E+11 O m (ng)9400* Oncogene size1950 MDCK genome size2.41E+09 I0I0 1 hcDNA (ng)1 Safety Factor1.16E+10 FDA Method Our Method  FDA method overestimates oncogenic risk by 19-fold  Reducing residual DNA with median size of 450 bp is adequate to mitigate oncogenic risk

48. Establishing Pre-filtration Bioburden Test Limit 48

49. Manufacture of a Sterile Drug Product 49  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. 50 eSlide - P4815 - MedImmune Template Measures to Mitigate Bioburden Risk  Pre-filtration testing  Filtration  Minimization of manufacturing hold times between process steps  Utilization of refrigerated storage for intermediates

51. 51

52. 52

53. Potential Limitations of EMA-Recommended Bioburden Limit, 10 CFU/100 mL  The limit has no scientific and statistical justifications  It protects neither consumer’s nor producer’s risk –Probability of rejecting a batch with 9 CFU/100 mL = 33.4% –Probability of accepting a batch with 11 CFU/100 mL = 50% 53

54. Additional Limitations of 10 CFU/100 mL Bioburden Limit  It does not take into account assay variability and the fact that microorganisms are not homogeneously distributed  Meeting or failing 10 CFU/100 mL acceptance limit may not provide adequate assurance that the true biobruden level is below or above 10 CFU/100 mL 54

55. A Risk-based Approach to Development of Bioburden Control and Pre-filtration Testing Strategy  Driven by product and process knowledge  Identification of types of risks, their associations with testing method and process parameters  Development of control strategy 55

56. Two Types of Risk Associated with Sterile Filtration Process  Drug solution with an unacceptable bioburden level passes the pre- filtration test  Breakthrough of bioburden through the final sterile filter  Both types of risk can be characterized thru probabilities of occurrence 56

57. Risk Associated with Three Different Test Schemes 57 20 CFU 32 CFU 63 CFU 5%

58. Mitigating Risk of Larger Number of Bioburden thru Sterial Filtration 58

59. Sterile Filtration 59  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)

60. Upper Bound of Probability p 0 for a CFU to Go Thru Sterile Filter (Yang, et al., 2013) 60

61. 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. 61

62. Risk of Bio-burden Breakthrough in Final Solution 62

63. Determination of Pre-filtration Sample Volume and Batch Size 63

64. Maximum Batch Sizes Based on Risks and Pre- filtration Test Schemes 64

65. A Few Additional Thoughts 65

66. 66 Actively Involve in Standard Setting  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) 66

67. Form Consortiums to Develop White/Concept Papers  A-Mab: a Case Study in Bioprocess Development  A-Vax: Applying Quality by Design to Vaccines 67

68. 68 eSlide - P4815 - MedImmune Template Conduct Innovative Statistical Research on Regulatory Issues  Solutions based on published methods are more likely accepted by regulatory agencies

69. Take a Good Statistical Lead in Resolving Regulatory Issues 69

70. Regularly Communicate with Regulatory Authorities 70

71. 71 eSlide - P4815 - MedImmune Template Conduct Joint Training

72. 72 eSlide - P4815 - MedImmune Template Q&A