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May 20, 2014

Harry Yang, Ph.D. MedImmune, LLC. Using Statistical Innovation to Impact Regulatory Thinking. May 20, 2014. How Do We Influence Regulatory Thinking?. An Old Tried and True Method. Throw statisticians at the deep end of regulatory interactions. An Old Tried and True Method (Cont’d).

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May 20, 2014

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

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

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

  4. 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

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

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

  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)

  8. 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 mi: Oncogene sizes M: Host genome size E[U]: Expected amount of residual hose DNA/dose

  9. Revised Safety Factor (Lewis et al., 2001) Om: Amount of oncogenes to induce an event I0: Number of oncogenes in host genome mi: 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

  11. Relationship between Enzyme Cutting Efficiency and Median DNA Size (Yang, et al., 2010) 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) I0: Number of oncogenes in host genome mi: Oncogene sizes M: Host genome size Med0: 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

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

  16. Establishing Pre-filtration Bioburden Test Limit

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

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

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

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

  21. Sterile Filtration • 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 107 CFU/cm2 of effective filter area (EFA)

  22. Upper Bound of Probability p0 for a CFU to Go Thru Sterile Filter (Yang, et al., 2013)

  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 D0 of the pre-filtration solution • By choosing the batch size, this probability can be bounded by a pre-specified small number δ.

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

  25. 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 eSlide - P4815 - MedImmune Template

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

  27. USP Bioassay Guidelines “Roadmap” chapter (to include glossary) 27 Originally USP <111> and EP 5.3 <111> was split into two chapters, USP <1032> Design and Development of Biological Assays and USP <1034> Analysis of Biological Assays <1033> Biological Assay Validation added to the suite

  28. 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) eSlide - P4815 - MedImmune Template

  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

  30. A Few Additional Thoughts

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

  32. Take a Good Statistical Lead in Resolving Regulatory Issues

  33. Regularly Communicate with Regulatory Authorities

  34. Conduct Joint Training eSlide - P4815 - MedImmune Template

  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.

  36. Q&A eSlide - P4815 - MedImmune Template

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