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

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Presentation on theme: "Current Statistical Issues in Dissolution Profile Comparisons Sutan Wu, Ph.D. FDA/CDER 5/20/2014 1."— Presentation transcript:

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

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

3 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 4 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. Backgrounds:

5 5 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 Dissolution Data

6 6 Current Methods for Dissolution Profile Comparisons

7 7 MethodsProsConsComments Simple to compute Clear Cut-off Point: 50 Only the mean dissolution profile to be considered; At least 3 same time point measurements for the test and reference batch; Only one measurement should be considered after 85% dissolution of both products; %CV <=20% at the earlier time points and <=10% at other time points. Approximately over 95% applications Bootstrapping f2 is used for data with large variability Mahalanobis Distance Both the mean profile and the batch variability to be considered together Simple stat formula Same time point measurements for the test and reference batches; Cut-off point not proposed A few applications Hard to have a common acceptable cut-off point Model-dependent Approach Measurements at different time points Model selection Cut-off point not proposed Some internal lab studies

8 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 9 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? Motivations:

10 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 11  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% ( 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 Simulation Cases:

12 12 Basic Simulation Structures:  Dissolution Mean Profile from Weibull Distribution:  Reference Batch: MDT= 25, B=1, Dmax=85  Testing Batch: StartEndStep MDT13372 B0.551.450.05 Dmax73972  Batch Variability (%CV) for 12 tablets: StartEndStep <=15 mins 5%50%2% >15 mins 5%30%2%  5000 iterations for Bootstrapping f2 Time (mins): 5, 10, 15, 20, 30, 45, 60

13 Scenarios 1 Cases: 13 %CV at all time points = 5% f243.60 Bootstrapping f243.30 M-Distance31.07 %CV at all time points = 10% f284.23 Bootstrapping f284.10 M-Distance2.81 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. f251.04 Bootstrapping f250.77 M-Distance9.18 %CV ( 15mins) = 12% Reference Testing

14 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 15 Scenarios 2 Cases: Different Mean Dissolution Profile, but same variability at all the time points: some board line cases show up 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? Dmax=89, MDT=19, B=0.75 %CV all time points 30% f250.10 Bootstrapping f2 49.46 M-Distance5.34 Dmax=89, MDT=19, B=0.85 %CV all time points 30% f251.3 Bootstrapping f250.54 M-Distance5.03 Dmax=89, MDT=19, B=0.75 %CV all time points 10% f250.40 Bootstrapping f2 50.10 M-Distance9.31

16 16 Same Mean Dissolution Profile but large variability for testing batch 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 In examined cases

17 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 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 19 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 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 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 22

23 23 Back Up

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


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