Fasheng Li Associate Director Pharmaceutical Statistics Worldwide RampD Pfizer Inc 37 th Annual MBSW Muncie IN May 20 2014 Dissolution routinely tested to provide in vitro drug release information ID: 613428
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Slide1
Statistical Evaluation of Dissolution for Specification Setting and Stability Studies
Fasheng Li
Associate Director, Pharmaceutical Statistics
Worldwide R&D
Pfizer, Inc
37
th
Annual MBSW
Muncie, IN
May 20, 2014Slide2
Dissolution routinely tested to provide in vitro drug release information
Drug development: prediction of in vivo drug release profilesQuality control: assessment of batch-to-batch consistencyDecision making during dissolution method and drug development Data based specification setting for USP <711> dissolution testDissolution monitoring on stabilityStatistical assessment integral to decision making process.
Motivation
2
2Slide3
Outline
Setting Extended Release Dissolution SpecificationsNumber of time points neededCase 1: Two-point specCase 2: Three-point specEvaluation of possible specificationsDissolution on StabilityNo significant linear trend observed
Non-linear trend observed
3
3Slide4
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 specificationsAre fewer than three time points sufficient?Are three time points enough?4
4Slide5
Dissolution Spec Setting – Case 1
Mean disso profiles of three typical batches of a sustained release drug productProposed to have specs at two time points (30 and 180 minutes)Team discussed to add a spec at either 15 or 60 minutesSpecs at 30 and 180 minutes
Add either 15 or 60 minutes?
5
5Slide6
Dissolution Spec Setting – Case 1
An empirical first-order two-parameter non-linear regression model fit to the release profilesGoodness-of-fit of the model evaluated by a coefficient of determination R2-type measureModel appropriateness evaluated by the lack-of-fit test
R
2 =
,
6
6
Release = A(1-e
-bt
) is a two-parameter Weibull modelSlide7
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 profileNo need to add a third time point for specificationTeam agreed to set disso specifications without a third point
,
Two-point spec
7
7Slide8
Dissolution Spec Setting – Case 2
Mean disso profiles of three typical batches of a extended release drug productOriginally 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
8Slide9
Dissolution Spec Setting – Case 2
An empirical three-parameter non-linear regression model (Weibull ) fit to the release profilesGoodness-of-fit of the model evaluated by a coefficient of determination R2-type measureModel appropriateness evaluated by the lack-of-fit test
R
2 =
,
The three-parameter Weibull model is sufficient and adequate to define dissolution profiles in this case
Recommend three-time points for dissolution specifications
Three-point spec
9
9Slide10
Brief Summary
, Three-point specifications are apparently more advantageous than six-point specifications:Cost SavingsSave 50% reducing from 6 to 3 time pointsQuicker 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.
0.98
6
= 89% with six time points
10
10Slide11
Evaluation of Dissolution Specifications
, After Determining Number of Time PointsEvaluate proposed dissolution specifications against USP <711> at each time pointRecommend 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
11Slide12
USP
<711> Dissolution Test 1212Acceptance Criteria for Extended Release Drug ProductsSlide13
Controlled release product specs:
@1 hour: <= 30%@4 hours: 40-60%@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
Evaluation of Dissolution Specifications
13
50000 simulations performed on 46 dissolution data sets to check probabilities of passing USP <711> stages (L1, L2, and L3)
Current three-point specifications
13Slide14
Evaluation of Dissolution Specifications
14SpecificationsStage% 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%
L1
100.0
15.2
96.2
7.6
L2
84.8
58.7
99.0
2.1
L3
41.3
87.0
99.8
0.7
Q1: <= 30%
Q4: 40-60%
Q24: >= 85%
L1
100.0
4.3
59.4
38.2
L2
95.7
17.4
87.1
19.1
L3
82.6
47.8
91.0
19.9
Proposed
Specs
Comparable
Specs
14Slide15
Controlled release product -
recommended specifications for new dissolution method@1 hour: <= 30%@4 hours: 35-55%@24 hours: >= 80%Evaluation of Dissolution Specifications15
Revised three-point specifications
Revised three-point specifications
Individual Dissolution Profiles
Mean Dissolution Profiles
15Slide16
Brief Summary – Disso Spec Setting
, The number of time points on dissolution profiles used for specification settingCan be justified by fitting a non-linear release modelBased 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
16Slide17
Dissolution on Stability
Dissolution usually monitored on stability as a numerical quality attributes with numeric specificationse.g. %Release at 6 hours should be between 40-60% Dissolution data may not have a significant linear trend along stability timeLinear trends not significantNon-linear trends observedHow to evaluate dissolution data on stability? Typical Q1E shelf life analysis not appropriate.
17
17Slide18
Dissolution on Stability –
No Linear Trend18No Statistically significant Linear Trend
18
Linear Trend
Clear linear trend for the chemical impurity data
ICH Q1E Analysis Appropriate
ICH Q1E Analysis is not meaningful
No overall statistically significant trend in dissolution at 10 hoursSlide19
19
Dissolution on Stability – No Linear TrendShelf-life predicted based on the major chemical impurities: Apply linear regression analyses following the ICH Q1E guidanceThe risk of failing dissolution on stability will be quantifiedMake sure the risk of failing dissolution spec is lowUtilize dissolution profiles tested at each of the stability time points
19Slide20
20
Dissolution on Stability – No Linear Trend20A 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% Slide21
Storage
ConditionStrengthNonlinear 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
0.0000
47.8, 59.7
Yes
0.012
3
94.8
0.0159
1.739
0.9960
0.0000
49.5, 60.6
Yes
0.003
4
95.7
0.0142
1.777
0.9964
0.0000
49.5, 60.1
Yes
0.003
5
96.2
0.0140
1.783
0.9960
0.0000
49.5, 60.8
Yes
0.003
6
94.4
0.0135
1.800
0.9955
0.0000
48.3, 60.1
Yes
0.003
7
93.6
0.0107
1.867
0.9945
0.0000
44.5
, 57.4
Not the lower limit
0.865
30°C/75%RH
1
95.8
0.0180
1.714
0.9932
0.0000
50.9,
65.3
Not the upper limit
0.669
2
94.4
0.0150
1.764
0.9952
0.0000
48.8, 60.9
Yes
0.003
3
95.7
0.0165
1.734
0.9940
0.0000
49.7, 63.4
Yes
0.074
4
97.5
0.0149
1.756
0.9951
0.0000
49.6, 62.1
Yes
0.012
5
96.6
0.0137
1.796
0.9950
0.0000
49.2, 62.0
Yes
0.007
6
96.1
0.0132
1.811
0.9952
0.0000
48.9, 61.3
Yes
0.003
7
95.1
0.0108
1.867
0.9955
0.0000
46.2, 57.9
Yes
0.112
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%
Risk of Dissolution on Stability
21Slide22
22
Dissolution on Stability – No Linear Trend
Risk of failing disso on stability is < 0.9%Slide23
23
Dissolution on Stability – No Linear Trend
Risk of failing disso on stability is < 0.7%Slide24
Dissolution on Stability –
Non-linear Trend2424Extended release product: with a clear non-linear trend for dissolution data at x hoursSlide25
Dissolution on Stability –
Non-linear Trend2525Empirical 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)Slide26
Dissolution on Stability –
Non-linear Trend2626Stability program started at 7.4 months of manufacturing agePredicted shelf life is about 54.5 -7.4 = 47.1 monthsSlide27
Brief Summary – Dissolution on Stability
, Stability dissolution data often show no significant linear trends No significant linear or non-linear trendDissolution profile data can be utilized to remediate the risk of meeting dissolution specificationsMore 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
27Slide28
Summary
28Dissolution for extended release drug products facing decision makings in areas such asSetting SpecificationsNumber of time points on the profile for specSpecification limits at the time pointsDissolution on StabilityNo significant linear trendNon-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 scientistsSlide29
Acknowledgment
29Kim Vukovinsky, Senior Director, Pharmaceutical Statistics, Worldwide R&D, Pfizer Inc.