Beyond ICH Q1E Opening Remarks

1. Beyond ICH Q1EOpening Remarks Rebecca Elliott Senior Research Scientist Eli Lilly and Company MBSW 2013

2. ICH Q1E • Required analysis for setting specifications • Statistical details • BUT what does the analysis tell us? • The more data, the narrower the interval on the regression line, the longer the dating. • Assuming common slopes, the analysis provides an average change for a PRODUCT.

3. Estimate of Dating Dating is 22 months.

4. Estimate of Dating with More Data • Dating is now 24 months. • (Assuming common slopes.) • Slope represents average change across batches. • Batches are a random sample from product. • Slope represents average change for the PRODUCT.

5. Estimate of Dating with More Data • AND, we already know some batch results are likely to be outside of spec. • Observed • Projected

6. Why Do ICH Q1E? • Batches are released and evaluated individually. • Individual results must meet specs. • Dating/specifications need to apply to actual test results. • ICH Q1E does not provide analysis for individual results. • ICH Q1E does not consider additional circumstances that can cause molecule to degrade. • Shipping • Patient/customer use

7. Session Agenda • Jim Schwenke • Consulting Statistician, Applied Research Consultants, PQRI • On the Shelf-Life of Pharmaceutical Products • Jeff Gardner • President and Principal Consultant, DataPharm Statistical & Data Management Services • Statistical Considerations for Mitigating the Riskof Individual OOS Results on Stability • Becky Elliott • Senior Research Scientist, Eli Lilly and Company • Change During Patient Use—Questions and Challenges • Question and Answer Period

8. Change During Patient Use—Questions and Challenges Rebecca Elliott Senior Research Scientist Eli Lilly and Company MBSW 2013

9. Stability Model Release buffer is for assay variability Is this picture complete? Release buffer is for change, change variability, assay variability

10. More Complex Stability Model Multi-use products Controlled stability chamber Patient use Release buffer: normal change & variability, assay variability, and in-use change

11. In-Use Change Can be large Potentially fewer batches for analysis Can have a different change model than routine stability

12. Statistics are “easy” • Determine routine and in-use models • Linear • Quadratic • Nonlinear • Determine estimates of variability • Model • Assay • Adjust release buffer(s) Non-statistical questions are “hard.”

13. Today’s Topics • Modeling in-use change • Complexity of complete statistical model and impact on business • Significance of in-use change • Correlation of results • Groups • Proxy data • Other uncontrolled conditions

14. #1 Analysis Impact to Business • One-time or yearly studies? • Requirement is often upon registration • “Fresh” batch • “Aged” batch • There may be no regulatory requirement to generate data yearly • One-time estimate or yearly update? • Implications are to WHO does stat analysis WHEN and HOW. • One complicated model • Two easier models

15. #2 Significance of Change • Change estimates • Errors can be high depending on assay • Is change significant? • Include estimate of change variability?

16. #2 Significance of Change Assay variability is included in buffer for long term stability change. Is it “double counting” to include it for in-use change? Is there “room” within the specifications?

17. #2 Significance of Change p-value = 0.02 p-value = 0.06 Is change meaningful? Science vs. statistical significance

18. #3 Correlation in Results • Multiple batches can be manufactured close together in time (e.g., validation batches, special studies). • Timepoints to be assayed are close together. • Lab wants to maximize resources. • Hold samples • Test them together • Common timepoints across batches are put on same assay run. Testing batches together  dependent slopes

19. #3 Correlation—Shared Assay Dates Are these 4 independent estimates of the slope?

20. #3 Correlation—Solution • Backload samples • E.g.: 30 day study tested on day 0, 7, 15, 30 • Study day 1: put 30-day samples on stability • Study day 15: put 15-day samples on stability • Study day 23: put 7-day samples on stability • Study day 30: test all samples on same assay run • Independent slope estimates without run-to-run assay variability • More planning with lab • Protocols are more complicated

21. #4 Groups • What about group differences? • Sites, components, raw materials? • Different testing labs • Do we have “enough” data to tell meaningful differences? • Should we expect group differences?

22. #4 Groups—Are they different? Group x age effect p-value < 0.0001 No technical or scientific reason for these groups to be different. Therefore, there is no practical difference here. Sums of squares is small due to low variability within batches.

23. #5 Proxy Data • Patient use involves simulating dosing regimen • Does this impact the molecule? • Accelerated studies may be held under the same ambient conditions as patient use • Do these studies have same change? • What are timepoints? Are there enough during the in-use period?

24. In-Use Change • Non-statistical questions can impact • Conclusions • Analysis • Cost to the business

25. Other Uncontrolled Environments • Manufacturing wait times • Transfer times between production steps • Transfer times to packaging • Packaging/labeling time • Transfer time shipping • Shipping excursions • When in the process are stability samples assayed?

26. Estimating Routine Stability Change

27. Estimating Routine Stability Change

28. Estimating Routine Stability Change

29. Estimating Routine Stability Change

30. Estimating Routine Stability Change Where is time 0 sample drawn? Are we missing changes?

31. Conclusions • Estimating stability change goes beyond statistical computations. • Consider business processes • Impact to statistical modeling • Consider data structure • Correlated data points • Data groups • Consider science AND statistical significance • Consider proxy data