1. Statistical Evaluation of Dissolution for Specification Setting and Stability Studies Fasheng Li Associate Director, Pharmaceutical Statistics Worldwide R&D Pfizer, Inc 37th Annual MBSW Muncie, IN May 20, 2014

2. Motivation
Dissolution routinely tested to provide in vitro drug release information
Drug development: prediction of in vivo drug release profiles
Quality control: assessment of batch-to-batch consistency
Decision making during dissolution method and drug development
Data based specification setting for USP <711> dissolution test
Dissolution monitoring on stability
Statistical assessment integral to decision making process. 2

3. Outline Setting Extended Release Dissolution Specifications
Number of time points needed
Case 1: Two-point spec
Case 2: Three-point spec
Evaluation of possible specifications
Dissolution on Stability
No significant linear trend observed
Non-linear trend observed 3

4. Dissolution Specification Setting
How many time points are necessary for setting dissolution specifications?
Based on “Guidance for Industry: Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro/In Vivo Correlations”, at least three points (early, middle, late) on the dissolution profile should be used to have specifications
Are fewer than three time points sufficient?
Are three time points enough? 4

5. Dissolution Spec Setting – Case 1
Mean disso profiles of three typical batches of a sustained release drug product
Proposed to have specs at two time points (30 and 180 minutes)
Team discussed to add a spec at either 15 or 60 minutes
Specs at 30 and 180 minutes
Add either 15 or 60 minutes? 5

6. Dissolution Spec Setting – Case 1,
An empirical first-order two-parameter non-linear regression model fit to the release profiles
Goodness-of-fit of the model evaluated by a coefficient of determination R2-type measure
Model appropriateness evaluated by the lack-of-fit test
R2 =
Release = A(1-e-bt) is a two-parameter Weibull model 6

7. Dissolution Spec Setting – Case 1,
Dissolution profiles defined well by a two-parameter release model
Mathematically, any two points on the profile would be able to sufficiently define the release profile
No need to add a third time point for specification
Team agreed to set disso specifications without a third point
Two-point spec 7

8. Dissolution Spec Setting – Case 2
Mean disso profiles of three typical batches of a extended release drug product
Originally specs at 5 time points proposed; should 1 more be added to improve control?
Question: How many time points are needed for setting dissolution specifications? 8

9. Dissolution Spec Setting – Case 2,
An empirical three-parameter non-linear regression model (Weibull ) fit to the release profiles
Goodness-of-fit of the model evaluated by a coefficient of determination R2-type measure
Model appropriateness evaluated by the lack-of-fit test
Three-point spec
R2 =
The three-parameter Weibull model is sufficient and adequate to define dissolution profiles in this case
Recommend three-time points for dissolution specifications 9

10. 0.983 = 94% with three time points
Brief Summary ,
Three-point specifications are apparently more advantageous than six-point specifications:
Cost Savings
Save 50% reducing from 6 to 3 time points
Quicker Analytical Results
Conformity Risk Reduction: Assuming the probability of passing USP <711> dissolution test at each time point is the same (e.g. 98%), the overall probability to pass:
0.983 = 94% with three time points
vs.
0.986 = 89% with six time points 10

11. Evaluation of Dissolution Specifications,
After Determining Number of Time Points
Evaluate proposed dissolution specifications against USP <711> at each time point
Recommend new dissolution specifications if necessary
Statistical Approach
Simulations performed on individual dissolution data at each of the specification time points to check the probabilities of passing different stages (L1, L2, and L3) of USP <711> dissolution test 11

12. USP <711> Dissolution Test
Acceptance Criteria for Extended Release Drug Products 12

13. Evaluation of Dissolution Specifications
Controlled release product specs:
@1 hour: <= 30%
@4 hours: %
@24 hours: >= 80%
Re-evaluate specs due to method change
Data: 46 unique dissolution conditions
Each have 6 to 96 individual disso profiles
A total of 1578 disso profiles collected
Current three-point specifications
50000 simulations performed on 46 dissolution data sets to check probabilities of passing USP <711> stages (L1, L2, and L3) 13

14. Acceptance Probability
Evaluation of Dissolution Specifications
Specifications
Stage
% of Disso Tests
Acceptance Probability
by Stage Lx
Need Stage Lx
Pass 100% by Stage Lx
Mean
StDev
Q1: <= 25% Q4: 35-55% Q24: >= 80% L1
100.0
13.0
95.3
9.0 L2
87.0
52.2
98.5
2.9 L3
47.8
80.4
99.5
1.4
Q1: <= 30% Q4: 35-55% Q24: >= 80%
15.2
96.2
7.6
84.8
58.7
99.0
2.1
41.3
99.8
0.7
Q1: <= 30% Q4: 40-60% Q24: >= 85%
4.3
59.4
38.2
95.7
17.4
87.1
19.1
82.6
91.0
19.9
Comparable
Specs
Proposed Specs 14

15. Evaluation of Dissolution Specifications
Controlled release product - recommended specifications for new dissolution method
@1 hour: <= 30%
@4 hours: %
@24 hours: >= 80%
Individual Dissolution Profiles
Revised three-point specifications
Mean Dissolution Profiles
Revised three-point specifications 15

16. Brief Summary – Disso Spec Setting,
The number of time points on dissolution profiles used for specification setting
Can be justified by fitting a non-linear release model
Based on the number of parameters of the non-linear release model
Specifications at each time points
Can be evaluated by performing simulations on dissolution data against USP <711> criteria
Calculate the probability to pass USP criteria 16

17. Dissolution on Stability
Dissolution usually monitored on stability as a numerical quality attributes with numeric specifications
e.g. %Release at 6 hours should be between 40-60%
Dissolution data may not have a significant linear trend along stability time
Linear trends not significant
Non-linear trends observed
How to evaluate dissolution data on stability? Typical Q1E shelf life analysis not appropriate. 17

18. Dissolution on Stability – No Linear Trend
Clear linear trend for the chemical impurity data
ICH Q1E Analysis Appropriate
Linear Trend
ICH Q1E Analysis is not meaningful
No overall statistically significant trend in dissolution at 10 hours
No Statistically significant
Linear Trend 18

19. Dissolution on Stability – No Linear Trend
Shelf-life predicted based on the major chemical impurities: Apply linear regression analyses following the ICH Q1E guidance
The risk of failing dissolution on stability will be quantified
Make sure the risk of failing dissolution spec is low
Utilize dissolution profiles tested at each of the stability time points 19

20. Dissolution on Stability – No Linear Trend
A three-parameter Weibull model:
%Release = A(1-exp(-b*tm))
fit to all mean or individual dissolution profiles at each of the stability time points for all registration stability batches
The risk of failing dissolution at a future stability test time since time is not relevant can be quantified by
Constructing prediction limits with confidence level p%
Checking the limits against the spec of (45, 65)
If the prediction limits are within the spec limits, the risk of failing a future average dissolution would be no more than 100-p% 20

21. Risk of Dissolution on Stability
Model fit mean dissolution profiles of stability times points very well (R2 > 0.99)
The risk of failing dissolution test on stability at a future time is no more than 0.9%
Storage Condition
Strength
Nonlinear Model Parameters and Fit Statistics
99 % Pred Limits for Dissolution at 10hr
99 % Pred Limits Meet Spec (45, 65)?
%Chance for a Future Disso Test to Fail A b m R2
P-value
25°C/60%RH 1
94.5
0.0176
1.711
0.9939
0.0000
49.6, 63.0
Yes
0.045 2
94.0
0.0147
1.760
0.9952
47.8, 59.7
0.012 3
94.8
0.0159
1.739
0.9960
49.5, 60.6
0.003 4
95.7
0.0142
1.777
0.9964
49.5, 60.1 5
96.2
0.0140
1.783
49.5, 60.8 6
94.4
0.0135
1.800
0.9955
48.3, 60.1 7
93.6
0.0107
1.867
0.9945
44.5, 57.4
Not the lower limit
0.865
30°C/75%RH
95.8
0.0180
1.714
0.9932
50.9, 65.3
Not the upper limit
0.669
0.0150
1.764
48.8, 60.9
0.0165
1.734
0.9940
49.7, 63.4
0.074
97.5
0.0149
1.756
0.9951
49.6, 62.1
96.6
0.0137
1.796
0.9950
49.2, 62.0
0.007
96.1
0.0132
1.811
48.9, 61.3
95.1
0.0108
46.2, 57.9
0.112 21

22. Dissolution on Stability – No Linear Trend
Risk of failing disso on stability is < 0.9% 22

23. Dissolution on Stability – No Linear Trend
Risk of failing disso on stability is < 0.7% 23

24. Dissolution on Stability – Non-linear Trend
Extended release product: with a clear non-linear trend for dissolution data at x hours 24

25. Dissolution on Stability – Non-linear Trend
Empirical model of:
%Release at x hours = A(1-e-b*(t+t0))
can be fit to dissolution at x hours from manufacturing age for all registration stability batches
Shelf life (in terms of manufacture age) can be predicted when the 95% confidence interval intersects with the spec limits
Shelf life (in terms of stability storage age) can be determined by subtracting the manufacturing age of the initial stability time point (stability time 0 month) 25

26. Dissolution on Stability – Non-linear Trend
Stability program started at 7.4 months of manufacturing age
Predicted shelf life is about = 47.1 months 26

27. Brief Summary – Dissolution on Stability,
Stability dissolution data often show no significant linear trends
No significant linear or non-linear trend
Dissolution profile data can be utilized to remediate the risk of meeting dissolution specifications
More information used versus evaluate at 1 time point on the profile
Non-linear trend
Empirical non-linear model fit to stability data could help justify the prediction of shelf life 27

28. Summary
Dissolution for extended release drug products facing decision makings in areas such as
Setting Specifications
Number of time points on the profile for spec
Specification limits at the time points
Dissolution on Stability
No significant linear trend
Non-linear trend
Statistics will be able to contribute greatly in the above areas to make regulatory appealing decisions
Statisticians need to work proactively with team scientists

29. Acknowledgment
Kim Vukovinsky, Senior Director, Pharmaceutical Statistics, Worldwide R&D, Pfizer Inc.