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Current Statistical Issues in Dissolution Profile Comparisons

Sutan Wu, Ph.D.

FDA/CDER

5/20/2014

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Outlines:

• Background of Dissolution Profile Comparisons

• Current Methods for Dissolution Profile Comparisons

• Current Statistical Concerns

• Simulation Cases

• Discussions

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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.

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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:

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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

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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

Current Methods for Dissolution Profile Comparisons

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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 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

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Some Review Lessions:

0 15 30 45 60 750

15

30

45

60

Similary Factor f2

Bootstrapping f2

• 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.

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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 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:

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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)?

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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 variability

Scenarios 3: multiple measurements after 85% dissolution

“Safe” Variability cases: Dissolution Guidance recommendations

Large Variability cases

Simulation Cases:

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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 13 37 2

B 0.55 1.45 0.05

Dmax 73 97 2

Batch Variability (%CV) for 12 tablets:

Start End Step

<=15 mins

5% 50% 2%

>15 mins

5% 30% 2%

)],

0 10 20 30 40 50 60 700

10

20

30

40

50

60

70

80

90

Ref Batch

Testing Batch 1

Testing Batch 2

Time in Mins

Dis

so

luti

on

(%

)

5000 iterations for Bootstrapping f2

Time (mins): 5, 10, 15, 20, 30, 45, 60

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Scenarios 1 Cases:

%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 51.04

Bootstrapping f2 50.77

M-Distance 9.18

%CV (<=15mins) = 15%, %CV (> 15mins) = 12%

Reference

Testing

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0 5 10 15 20 25 300

25

50

75

100

M-Distance vs. Bootstrapping f2

M-Distance

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ots

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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.

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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 f2 49.46

M-Distance 5.34

Dmax=89, MDT=19, B=0.85

%CV all time points 30%

f2 51.3

Bootstrapping f2 50.54

M-Distance 5.03

Dmax=89, MDT=19, B=0.75

%CV all time points 10%

f2 50.40

Bootstrapping f2 50.10

M-Distance 9.31

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Same Mean Dissolution Profile but large variability for testing batch

0 10 20 30 40 50 60 700

10

20

30

40

50

60

70

80

90

Testing Batch

Ref 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

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Scenarios 3: More than 1 measurement over 85%

0 10 20 30 40 50 60 700

10

20

30

40

50

60

70

80

90

100

Testing BatchRef Batch

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.

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

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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.

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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

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Acknowledgement:

FDA Collaborators and Co-workers:

• ONDQA: Dr. John Duan, Dr. Tien-Mien Chen

• OGD: Dr. Pradeep M. Sathe

• OB: Dr. Yi Tsong

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THANK YOU!

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Back Up

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