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
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.
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2
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
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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?
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4
Dissolution Spec Setting – Case 1
 Mean disso
profiles of three
typical batches of
a sustained
release drug
product
Specs at 30 and
180 minutes
 Proposed to have
specs at two time
points (30 and 180
minutes)
 Team discussed to
add a spec at
either 15 or 60
minutes
Add either 15 or 60
minutes?
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5
Dissolution Spec Setting – Case 1
,
 An empirical
first-order twoparameter nonlinear regression
model fit to the
release profiles
 Goodness-of-fit
of the model
evaluated by a
coefficient of
determination
R2-type measure
R2 = 1 
SSE
SSTotal
 Model
appropriateness
evaluated by the
lack-of-fit test
Release = A(1-e-bt) is a two-parameter Weibull model
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6
,
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
Two-point
spec
 Team agreed to set
disso specifications
without a third
point
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7
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?
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8
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
R2 = 1 
Three-point
spec
SSE
SSTotal
 Model
appropriateness  The three-parameter Weibull model is sufficient and
adequate to define dissolution profiles in this case
evaluated by the
 Recommend three-time points for dissolution
lack-of-fit test
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specifications
9
,
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
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,
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
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USP <711> Dissolution Test
Acceptance Criteria for Extended Release Drug Products
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12
Evaluation of Dissolution Specifications
 Controlled release
product specs:
 @1 hour: <= 30%
 @4 hours: 40-60%
 @24 hours: >= 80%
 Re-evaluate specs due
to method change
Current three-point
specifications
 Data: 46 unique
dissolution conditions
 Each have 6 to 96
individual disso profiles
 A total of 1578 disso
profiles collected
50000 simulations performed on 46 dissolution data
sets to check probabilities of passing USP <711>
stages (L1, L2, and L3)
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Evaluation of Dissolution Specifications
% of Disso Tests
Acceptance Probability
by Stage Lx
Specifications Stage Need Stage Lx Pass 100% by Stage Lx
100.0
13.0
Q1: <= 25% L1
87.0
52.2
Q4: 35-55% L2
Q24: >= 80% L3
47.8
80.4
100.0
15.2
Q1: <= 30% L1
84.8
58.7
Q4: 35-55% L2
Q24: >= 80% L3
41.3
87.0
100.0
4.3
Q1: <= 30% L1
95.7
17.4
Q4: 40-60% L2
Q24: >= 85% L3
82.6
47.8
Mean
95.3
98.5
99.5
96.2
99.0
99.8
59.4
87.1
91.0
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StDev
9.0
2.9
1.4
7.6
2.1
0.7
38.2
19.1
19.9
Comparable
Specs
Proposed
Specs
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Evaluation of Dissolution Specifications
Controlled release product recommended specifications for new
dissolution method
Individual
Dissolution Profiles
 @1 hour: <= 30%
 @4 hours: 35-55%
 @24 hours: >= 80%
Revised three-point
specifications
Mean Dissolution
Profiles
Revised three-point
specifications
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,
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
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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.
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Dissolution on Stability – No 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 hours
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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
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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
1. Constructing prediction limits with confidence level p%
2. Checking the limits against the spec of (45, 65)
3. If the prediction limits are within the spec limits, the risk
of failing a future average dissolution would be no more
than 100-p%
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• 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
% Release  A(1 - e
Storage
Condition
25°C/60%RH
30°C/75%RH
-bt
m
)
Nonlinear Model Parameters and
Fit Statistics
99 % Pred
Limits for
Dissolution at
P-value
10hr
99 % Pred Limits
%Chance for a
Meet
Future Disso Test
Spec (45, 65)?
to Fail
Strength
A
b
m
R2
1
2
3
4
5
6
94.5
94.0
94.8
95.7
96.2
94.4
0.0176
0.0147
0.0159
0.0142
0.0140
0.0135
1.711
1.760
1.739
1.777
1.783
1.800
0.9939
0.9952
0.9960
0.9964
0.9960
0.9955
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
49.6, 63.0
47.8, 59.7
49.5, 60.6
49.5, 60.1
49.5, 60.8
48.3, 60.1
Yes
Yes
Yes
Yes
Yes
Yes
0.045
0.012
0.003
0.003
0.003
0.003
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93.6 0.0107 1.867 0.9945 0.0000
44.5, 57.4
Not the lower limit
0.865
1
2
3
4
5
6
95.8
94.4
95.7
97.5
96.6
96.1
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
50.9, 65.3
48.8, 60.9
49.7, 63.4
49.6, 62.1
49.2, 62.0
48.9, 61.3
Not the upper limit
Yes
Yes
Yes
Yes
Yes
0.669
0.003
0.074
0.012
0.007
0.003
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95.1 0.0108 1.867 0.9955 0.0000
46.2, 57.9
Yes
0.112
0.0180
0.0150
0.0165
0.0149
0.0137
0.0132
1.714
1.764
1.734
1.756
1.796
1.811
0.9932
0.9952
0.9940
0.9951
0.9950
0.9952
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Dissolution on Stability – No Linear Trend
Risk of failing disso on
stability is < 0.9%
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Dissolution on Stability – No Linear Trend
Risk of failing disso on
stability is < 0.7%
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Dissolution on Stability – Non-linear Trend
Extended release
product: with a
clear non-linear
trend for
dissolution data
at x hours
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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)
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Dissolution on Stability – Non-linear Trend
 Stability
program started
at 7.4 months of
manufacturing
age
 Predicted shelf
life is about
54.5 -7.4 = 47.1
months
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,
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
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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
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Acknowledgment
Kim Vukovinsky, Senior Director, Pharmaceutical Statistics,
Worldwide R&D, Pfizer Inc.
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