Current Statistical Issues in Dissolution Profile Compariso - PowerPoint Presentation

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Current Statistical Issues in Dissolution Profile Comparisons

Sutan Wu, Ph.D.FDA/CDER5/20/2014

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Outlines:

Background of Dissolution Profile ComparisonsC

urrent Methods for Dissolution Profile Comparisons

Current Statistical ConcernsSimulation CasesDiscussions

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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.Slide4

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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:Slide5

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Recorded

at multiple time

pointsAt least 12 tablets at each selected time point is recommendedProfile curves are drug-dependent

e.g

: Immediate release vs. extend

release

Response: cumulative percentage in dissolution

Dissolution DataSlide6

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

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Current Methods for Dissolution Profile ComparisonsSlide7

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Methods

Pros

Cons

Comments

Similarity factor

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.Approximatelyover 95% applications Bootstrapping f2 is used for data with large variabilityMahalanobis

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 proposedA few applications Hard to have a common acceptable cut-off pointModel-dependent ApproachMeasurements at different time pointsModel selectionCut-off point not proposedSome internal lab studies

MethodsProsConsCommentsSimple 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% applications Bootstrapping f2 is used for data with large variabilityMahalanobis DistanceBoth the mean profile and the batch variability to be considered togetherSimple stat formula Same time point measurements for the test and reference batches;Cut-off point not proposedA few applications Hard to have a common acceptable cut-off pointModel-dependent ApproachMeasurements at different time pointsModel selectionCut-off point not proposedSome internal lab studiesSlide8

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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.Slide9

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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

detectionUpper Bound of the 90% 2-sided confidence interval (Tsong et. al. 1996)Subsequent Concerns: The validity of M-Distance? The cut-off point?Motivations: Slide10

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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)?Slide11

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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 variabilityScenarios 3: multiple measurements

after 85%

dissolution

“Safe” Variability cases: Dissolution Guidance recommendations

Large Variability cases

Simulation Cases:Slide12

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Basic Simulation Structures:Dissolution Mean Profile from Weibull

Distribution:

Reference Batch: MDT= 25, B=1, Dmax=85

Testing Batch

:

Start

End

Step

MDT

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2

B0.551.450.05Dmax73972Batch Variability (%CV) for 12 tablets: StartEndStep<=15 mins5%50%2%>15 mins5%30%2%

)],

5000 iterations for Bootstrapping f2

Time (

mins

): 5, 10, 15, 20, 30, 45, 60 Slide13

Scenarios 1 Cases:

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%CV at all time points = 5%

f2

43.60

Bootstrapping f2

43.30

M-Distance

31.07

%CV at all time points = 10%

f2

84.23

Bootstrapping f2

84.10

M-Distance

2.81

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. f2

51.04Bootstrapping f250.77M-Distance9.18%CV (<=15mins) = 15%, %CV (> 15mins) = 12%ReferenceTestingSlide14

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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. Slide15

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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%

f2

50.10

Bootstrapping f249.46M-Distance5.34

Dmax

=89, MDT=19, B=0.85

%CV all time points 30%

f2

51.3Bootstrapping f250.54M-Distance5.03

Dmax=89, MDT=19, B=0.75%CV all time points 10%f2 50.40Bootstrapping f250.10M-Distance9.31Slide16

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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 casesSlide17

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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.Slide18

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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.

T

he cut-off point is required for further deep examinations, particularly, M-Distance values at different batch variability and bootstrapping f2 around 50.Slide19

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Problems encountered with M-distance: Convergence issue with Inverse of

 Proposal: To c

ompute 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.Slide20

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References:FDA Guidance: Dissolution Testing of

Immediate Release

Solid Oral Dosage Forms, 1997FDA 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, 1997In Vitro Dissolution Profile Comparison, Tsong et. al, 2003Assessment 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, 1996Slide21

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Acknowledgement:FDA Collaborators and Co-workers:

ONDQA: Dr. John Duan,

Dr. Tien-Mien ChenOGD: Dr. Pradeep M. SatheOB: Dr. Yi TsongSlide22

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Thank You!Slide23

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Back UpSlide24

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90% Confidence Region of M-Distance:

,where

By Langrage Multiplier Method