Presentation on theme: "Current Statistical Issues in Dissolution Profile Comparisons"— Presentation transcript:
1 Current Statistical Issues in Dissolution Profile Comparisons Sutan Wu, Ph.D.FDA/CDER5/20/2014
2 Outlines:Background of Dissolution Profile ComparisonsCurrent Methods for Dissolution Profile ComparisonsCurrent Statistical ConcernsSimulation CasesDiscussions
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 processComparison between batches of pre-change and post-change under certain post-change conditionse.g.: add a lower strength, formulation change, manufacturing sitechangeGeneric Drug EvaluationsFDA 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 recommendedProfile curves are drug-dependente.g: Immediate release vs. extend releaseResponse: cumulative percentage in dissolution
6 Current Methods for Dissolution Profile Comparisons Model-Independent ApproachesSimilarity factor 𝑓 2 (FDA Dissolution Guidance):Multivariate Confidence Region Procedure --- Mahalanobis Distance:𝐷 𝑀 = ( 𝑹 𝑡 − 𝑻 𝑡 )′ Σ 𝑝𝑜𝑜𝑙𝑒𝑑 −1 ( 𝑹 𝑡 − 𝑻 𝑡 )Σ 𝑝𝑜𝑜𝑙𝑒𝑑 = Σ 𝑡𝑒𝑠𝑡 + Σ 𝑟𝑒𝑓 2 , 𝑹 𝑡 = 𝑅 1 ,…. 𝑅 𝑡 ′ , 𝑻 𝑡 =( 𝑇 1 , …. 𝑇 𝑡 )′Model-Dependent Approaches:Select the most appropriate model such as logit, Weibull to fit the dissolution dataCompare the statistical distance among the model parameters
7 Model-dependent Approach MethodsProsConsCommentsSimilarity factor 𝑓 2Simple to computeClear Cut-off Point: 50Only 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.Approximatelyover 95% applicationsBootstrapping f2 is used for data with large variabilityMahalanobis DistanceBoth the mean profile and the batch variability to be considered togetherSimple stat formulaSame time point measurements for the test and reference batches;Cut-off point not proposedA few applicationsHard to have a common acceptable cut-off pointModel-dependent ApproachMeasurements at different time pointsModel selectionSome internal lab studies
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%) forf2 index50 could be the cut-off pointSubsequent 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 detectionUpper 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 %CV > 20% (<= 15 mins) or/and %CV > 10% (> 15mins) Simulation Cases:Scenarios 1: similarity factor f2 “safe” casesFor 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% dissolutionScenarios 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 batchesSame mean dissolution profile but testing batch has large variabilityScenarios 3: multiple measurements after 85% dissolution“Safe” Variability cases: Dissolution Guidance recommendationsLarge Variability cases
13 Scenarios 1 Cases: Reference Testing %CV at all time points = 5% 43.60Bootstrapping f243.30M-Distance31.07f284.23Bootstrapping f284.10M-Distance2.81When 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%f251.04Bootstrapping f250.77M-Distance9.18
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 2 Cases:Different Mean Dissolution Profile, but same variability at all the time points: some board line cases show upDmax=89, MDT=19, B=0.85%CV all time points 30%Dmax=89, MDT=19, B=0.75%CV all time points 30%f250.10Bootstrapping f249.46M-Distance5.34f251.3Bootstrapping f250.54M-Distance5.03Dmax=89, MDT=19, B=0.75%CV all time points 10%Some discrepancies were observed between Bootstrapping f2 and f2 indexBootstrapping f2 gives different conclusions for the same mean profile but different batch variabilityValues of M-Distance vary: stratified by batch variability?f250.40Bootstrapping f250.10M-Distance9.31
16 Same Mean Dissolution Profile but large variability for testing batch In examined casesBootstrapping 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: Conclusions: 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 conclusionas 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 Σ 𝑝𝑜𝑜𝑙𝑒𝑑 ,Proposal: To compute the increment M-Distance𝑅 𝑖𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡_𝑡 = 𝑅 1 , 𝑅 2 − 𝑅 1 , …, 𝑅 𝑡−1 − 𝑅 𝑡𝑇 𝑖𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡_𝑡 =( 𝑇 1 , 𝑇 2 − 𝑇 1 , …, 𝑇 𝑡−1 − 𝑇 𝑡 )Σ 𝑖𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡_𝑅 =𝐶𝑜𝑣 𝑅 𝑖𝑛𝑐𝑟𝑒𝑚𝑒𝑛 𝑡 𝑡 , Σ 𝑖𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡_𝑇 =𝐶𝑜𝑣( 𝑇 𝑖𝑛𝑐𝑟𝑒𝑚𝑒𝑛 𝑡 𝑡 )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, 1997FDA Guidance: SUPAC for Immediate Release Solid Oral Dosage Forms, 1995FDA Guidance: Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro/In Vivo Correlation, 1997In Vitro Dissolution Profile Comparison, Tsong et. al, 2003Assessment of Similarity Between Dissolution Profiles, Ma et. al, 2000In Vitro Dissolution Profile Comparison – Statistics and Analysis of the Similarity Factor f2, V. Shah et. al, 1998Statistical 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 ChenOGD: Dr. Pradeep M. SatheOB: Dr. Yi Tsong