Current Statistical Issues in Dissolution Profile Comparisons

1. Current Statistical Issues in Dissolution Profile Comparisons Sutan Wu, Ph.D. FDA/CDER 5/20/2014

2. Outlines: • Background of Dissolution Profile Comparisons • Current Methods for Dissolution Profile Comparisons • Current Statistical Concerns • Simulation Cases • Discussions

3. Disclaimer: The presented work and views in this talk represents the presenter’s personal work and views, and do not reflect any views or policy with CDER/FDA.

4. Backgrounds: • Dissolution profile comparison: why so important? • Extensive applications throughout the product development process • Comparison between batches of pre-change and post-change under certain post-change conditions • e.g.: add a lower strength, formulation change, manufacturing site • change • Generic Drug Evaluations • FDA Guidance: Dissolution, SUPAC-SS, SUPAC-IR, IVIV and etc.

5. Dissolution Data • Recorded at multiple time points • At least 12 tablets at each selected time point is recommended • Profile curves are drug-dependent • e.g: Immediate release vs. extend release • Response: cumulative percentage in dissolution

6. Current Methods for Dissolution Profile Comparisons • Model-Independent Approaches • Similarity factor (FDA Dissolution Guidance): • Multivariate Confidence Region Procedure --- Mahalanobis Distance: • , • Model-Dependent Approaches: • Select the most appropriate model such as logit, Weibull to fit the dissolution data • Compare the statistical distance among the model parameters

8. Some Review Lessions: • Large variability was observed in some applications and the conclusions based on similarity factor f2 were in doubt. • Bootstrapping f2 was applied to re-evaluate the applications. Different conclusions were observed.

9. Motivations: • How to cooperate the variability consideration into dissolution profile comparison in a feasible and practical way? • Bootstrapping f2: • Lower bound of the non-parametric bootstrapping confidence interval (90%) for • f2 index • 50 could be the cut-off point • Subsequent Concerns: The validity of bootstrapping f2? • Mahalanobis-Distance (M-Distance): • A classical multivariate analysis tool for describing the distance between two vectors and widely used for outlier detection • Upper Bound of the 90% 2-sided confidence interval (Tsong et. al. 1996) • Subsequent Concerns: The validity of M-Distance? The cut-off point?

10. Objectives: • Thoroughly examine the performance of bootstrapping f2 and f2 index: can bootstrapping f2 save the situations that f2 is not applicable? • Gain empirical knowledge of the values of M-distance: does M-distance is a good substitute? What would be the “appropriate” cut-off point(s)?

11. Simulation Cases: • Scenarios 1: similarity factor f2 “safe” cases • For both batches 1) %CV at earlier time points (within 15 mins) <= 20% and %CV <= 10% at other time points; 2) Only one measurement after 85% dissolution • Scenarios 2: large batch variability cases (f2 is not recommended generally) • %CV > 20% (<= 15 mins) or/and %CV > 10% (> 15mins) • Different mean dissolution profile but same variability for both batches • Same mean dissolution profile but testing batch has large variability • Scenarios 3: multiple measurements after 85% dissolution • “Safe” Variability cases: Dissolution Guidance recommendations • Large Variability cases

12. Basic Simulation Structures: • Dissolution Mean Profile from Weibull Distribution: • Reference Batch: MDT= 25, B=1, Dmax=85 • Testing Batch: )], • Batch Variability (%CV) for 12 tablets: • 5000 iterations for Bootstrapping f2 Time (mins): 5, 10, 15, 20, 30, 45, 60

13. Scenarios 1 Cases: Reference Testing %CV at all time points = 5% %CV at all time points = 10% • When similarity factor f2 is applicable per FDA guidance, bootstrapping f2 and f2 give the same similar/dissimilar conclusions; • In examined cases, the values of bootstrapping f2 is close to f2 values, though slightly smaller; • Values of M-Distance could vary a lot, • but within expectations. %CV (<=15mins) = 15%, %CV (> 15mins) = 12%

14. Demo of M-distance vs. Bootstrapping f2: • Values of M-Distance vary a lot: • for higher Bootstrapping f2, M-Distance can be lower than 5; • for board line cases (around 50), M-Distance can vary from 7 to 20.

15. Scenarios 2Cases: • Different Mean Dissolution Profile, but same variability at all the time points: some board line cases show up Dmax=89, MDT=19, B=0.85 %CV all time points 30% Dmax=89, MDT=19, B=0.75 %CV all time points 30% Dmax=89, MDT=19, B=0.75 %CV all time points 10% • Some discrepancies were observed between Bootstrapping f2 and f2 index • Bootstrapping f2 gives different conclusions for the same mean profile but different batch variability • Values of M-Distance vary: stratified by batch variability?

16. Same Mean Dissolution Profile but large variability for testing batch In examined cases • Bootstrapping f2 is more sensitive to batch variability, but still gives the same conclusion with cut-off point as 50; • This may suggest to use a “higher” value as the cut-off point at large batch variability cases; • M-Distance varies: depends on the batch variability

17. Scenarios 3: More than 1 measurement over 85% • In examined cases, • Bootstrapping f2 gives more appealing value but still same conclusion with cut-off point as 50; • This may suggest to use a different value as cut-off point for bootstrapping f2.

18. Findings: • When similarity factor f2 is applicable per FDA Dissolution guidance, bootstrapping f2 and f2 give the same similar/dissimilar conclusions; • In the examined cases, • Bootstrapping f2 is more sensitive to batch variability or multiple >85% measurements; • However, with 50 as the cut-off points, bootstrapping f2 still gives the same conclusion • as similarity factor f2; • Values of M-Distance varies a lot and appears that <=3 could be a similar case, and over 30 could be a different case. Conclusions: • Based on current review experiences and examined cases, bootstrapping f2 is recommended when the similarity factor f2 is around 50 or large batch variability is observed; • At the large batch variability cases, new cut-off points may be proposed. Testing batches would be penalized by larger batch variability. • M-Distance is another alternative approach for dissolution profile comparisons. Its values also depends on the batch variability. The cut-off point is required for further deep examinations, particularly, M-Distance values at different batch variability and bootstrapping f2 around 50.

19. Problems encountered with M-distance: Convergence issue with Inverse of Proposal: To compute the increment M-Distance The proposed increment M-Distance can help us solve the convergence problem caused by highly correlated data (cumulative measurements); The interpretation of increment M-Distance: the distance between the increment vectors from the testing and reference batches.

20. References: • FDA Guidance: Dissolution Testing of Immediate Release Solid Oral Dosage Forms, 1997 • FDA Guidance: SUPAC for Immediate Release Solid Oral Dosage Forms, 1995 • FDA Guidance: Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro/In Vivo Correlation, 1997 • In Vitro Dissolution Profile Comparison, Tsong et. al, 2003 • Assessment of Similarity Between Dissolution Profiles, Ma et. al, 2000 • In Vitro Dissolution Profile Comparison – Statistics and Analysis of the Similarity Factor f2, V. Shah et. al, 1998 • Statistical Assessment of Mean Differences Between Dissolution Data Sets, Tsong et al, 1996

21. Acknowledgement: • FDA Collaborators and Co-workers: • ONDQA: Dr. John Duan, Dr. Tien-Mien Chen • OGD: Dr. Pradeep M. Sathe • OB: Dr. Yi Tsong

22. Thank You!

23. Back Up

24. 90% Confidence Region of M-Distance: ,where By Langrage Multiplier Method