pre-qualification program: priority medicines interchangeability of multi source drug products...
TRANSCRIPT
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Pre-qualification Program: Priority Medicines
Interchangeability of Multi Source Drug Products
SALOMON STAVCHANSKY, PH.D.ALCON CENTENNIAL PROFESSOR OF PHARMACEUTICS
THE UNIVERSITY OF TEXAS AT AUSTINCOLLEGE OF PHARMACY
AUSTIN, TEXAS [email protected]
Kiev, Ukraine, June 25-27, 2007
Interchangeability of Multisource Drug Products Containing Highly Variable Drugs
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Outline
• Background– What is a highly variable drug?– Present bioequivalence BE study approach– Disadvantages of present approach
• Bioequivalence Example of Highly Variable Drugs• Reference – scaled average BE approach
– Widening the bioequivalence limits
– scaling
• Simulation Studies• Summary and Conclusions
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Questions
• Why?– Have you ever had or heard of a therapeutic failure
• Where do we want to be?– No therapeutic failures and no adverse events
• What assumptions are we willing to make?– Multisource products are interchangeable with brand
products
• How sure do you want to be?– How to protect the consumer and the industry?
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DRUG DEVELOPMENT PROCESS AND REGULATIONS
VARIABILITY INDRUG RESPONSE
CLINICALPHARMACOLOGY
P'KINETICS
P'DYNAMICS
DOSE ADJUSTMENTFOR RISK GROUPS
PRODUCTQUALITY
DISSOLUTIONBA, BE
CONTROLLED CLINICAL TRIALS
SAMENESS
DOSE RANGE FORTARGET POPULATION
DOSE RANGE IN LABELDOSE INDIVIDUALIZATIONBaber, N.S., Br.J.Clin.Pharmacol.,42,545,1996
THERAPEUTIC INDEX
EFFICACYSAFETY
BIOPHARMACEUTICS
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Highly Variable Drug Characteristics
• Drugs with high within subject variability (CVwr) in bioavailability parameters AUC and/or Cmax ≥ 30%
• Non narrow therapeutic index drugs
• Represent about 10% of the drugs studied in vivo and reviewed by the OGD-FDA
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HVD Drug Products
• Highly Variable Drug Products in which the drug is not highly variable, but the product is of poor pharmaceutical quality
– High within-formulation variability
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Variability Due to Drug Substance and/or Drug Product
• Drug Substance– Variable absorption rate, extent– Low extent of absorption– Extensive pre-systemic metabolism
• Drug product– Formulation
• Inactive ingredient effects• Manufacturing effects
– Effects of Bioequivalence Study Conduct• Bioanalytical Assay Sensitivity• Suboptimal PK Sampling
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Summary of the issues
• High Probability that the BE parameters will vary when the same subject receives a highly variable drug on different occasions
• Because of high variability the risk is to reject a product that in reality is bioequivalent -- Industry Risk !
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FDA Study to Characterize Highly Variable Drugs in BE Studies: methods
• Collected data from 1127 acceptable BE studies, submitted– In 524 ANDAs– From 2003-2005 (3 years)
• Most sponsors used 2-way crossover studies– Used ANOVA Root Mean Square Error to estimate
within-subject variance
• Drug was classified as highly variable if RMSE ≥ 0.3 or 30%
Source: Barbara M. Davit AAPS/FDA Workshop 5/22/2077, Rockville, MD
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FDA Study to Characterize Highly Variable Drugs in BE Studies: results
• BE studies of HVD enrolled more study subjects than studies of drugs with low variability– Average N in studies of HVD = 47– Average N in studies of drugs with lower
variability = 33• Range 18 – 73 subjects
Source: Barbara M. Davit AAPS/FDA Workshop 5/22/2077, Rockville, MD
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FDA Study to Characterize Highly Variable Drugs in BE Studies: results
• 10% of studies evaluated were HVD; of these:– 52% of studies were consistently HVD– 16% were borderline
• RMSE was slightly above or below 0.3• Average across all studies
– For the remaining 32%, high variability occurred sporadically
• Not HVD in most BE studies
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Reasons for Inconsistent Variability in BE Studies
• Differences in formulations• Bioanalytical assay sensitivity• Demographic characteristics of subjects• Subjects with irregular plasma concentrations• Number of study subjects• Whether subjects were fasted or fed
Source: Barbara M. Davit AAPS/FDA Workshop 5/22/2077, Rockville, MD
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Present FDA Approach for BE of HVD
• ANDAs for HVD use the same study design for drugs with lower variability
• Two way crossover design
• Replicate study design
• Firms are encouraged to use sequential designs
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Present FDA Approach for BE of HVD
• HVD must meet same acceptance criteria as drugs with lower variability
• 90% CI of AUC and Cmax test/reference (T/R ratios) must fall within: 0.8-1.25 (80-125%)
• Statistical adjustment necessary if a sequential study design is used
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Is present FDA’s approach suitable for HVD?
Approach Disadvantage
Enrolled adequate # of subjects (N) to show BE in 2 way crossover study
Study may require larger N
If study underpowered
must do new study
Replicate design ( 4-period) study
High dropout rate; may need to enroll larger N
EXPENSIVE
Group sequential design Must specify in protocol a priori
Statistical AdjustmentSource: Barbara M. Davit AAPS/FDA Workshop 5/22/2077, Rockville, MD
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Background for NEW approach
ACPS Meeting, April 14, 2004: Discussion on Highly Variable Drugs
• Different approaches were considered, e.g., expansion of bioequivalence limits, and scaled average bioequivalence
• Committee favored scaled average bioequivalence over other approaches
• FDA working group was created; a research project to evaluate scaling was initiated
ACPS = Advisory Committee for Pharmaceutical Science
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The Width of the 90% Confidence Interval
• The width depends on:– Within subject variability WSV– The number of subjects in the study
• The wider the 90% CI, the more likely it is to fall outside the limits of 80-125%
• Highly variable drugs are a problem
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90%CIs & BE Limits
• Green– Low WSV (~15%)– Narrow 90%CI– Passes
• Red– High WSV (~35%)– Wide 90%CI– Lower bound <80%– Fails
125%
100%
80%
• GMR & the # subjects same in both cases
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Chlorpromazine:ANOVA-CV%
Study 1a Study 2b Study 3c
ln Cmax 42.3 39.9 37.2ln AUClast 34.8 36.6 33.0
aBioequivalence study, n=37 (3-period study) bPharmacokinetic study n=11 (solution, 3-period study) cPharmacokinetic study, n=9, CPZ with & without quinidine (2-period study)
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Ref-1 Ref-2Ref-1 Ref-2
6
66
6
13
13
13
13
27
27
27
27
7
7
7
7
16
16
16
16
20
20
20
20
Cmax AUClast
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Chlorpromazine (ABE3)3 x 37 Subjects
Measure GMR% CV% 90%CI
ln Cmax 115 42.3 99-133 ln AUClast 110 34.8 97-124ANOVA-2 (GLM)
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ANOVA-1 (GLM)
Measure T v R1 T v R2 R1v R2
ln Cmax 103 - 146 89 - 126 72 - 102ln AUClast 97 - 128 94 - 125 85 - 112
Chlorpromazine: 90%CIs
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Background
ACPS Meeting, October 6, 2006
Preliminary results of simulation study were presented
Committee was in favor of using a point estimate constraint with scaled average BE
Most members favored a minimum sample size of 24
ACPS = Advisory Committee for Pharmaceutical Science
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Research Project
Highly Variable Drugs (HVD) working group evaluated different scaling approaches and study designs. Outcome:
– Scaled average bioequivalence, based on within subject variability of reference*
*
Source: Sam H. Haidar: AAPS/FDA Workshop 5/22/2077, Rockville, MD
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Objective
Determine the impact of scaled average bioequivalence on the power (percent of studies passing) at different levels of within subject variability (CV%), and under different conditions.
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Methods
Study design:
• 3-way crossover, e.g., R T R• Sample sizes tested: 24 and 36• Within subject variability: 15% - 60% CV• Geometric mean ratio: 1 – 1.7
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Methods
Variables tested: • Impact of increasing within subject
variability
• Use of point estimate constraint (80-125%)
• σw0: 0.2 vs. 0.25 vs. 0.294
• Sample size: 24 vs. 36
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Methods
Statistical Analysis:
• Modified Hyslop model*• Number of simulations: 1 million (106)/test• Percent of studies passing was determined using
average bioequivalence (80-125% limits), and scaled average bioequivalence (limits determined as a function of reference within subject variability)
• Test performed under different conditions
*Hyslop et al. Statist. Med. 2000; 19:2885-2897. Hyslop’s model was modified by Donald Schuirmann
Source: Sam H. Haidar: AAPS/FDA Workshop 5/22/2077, Rockville, MD
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Impact of Within Subject Variability
• 15% CV• 30% CV• 60% CV
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1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7
Geometric Mean Ratio
0
20
40
60
80
100
Perc
en
t o
f S
tud
ies P
assin
gAverage vs. Scaled Average Bioequivalence
CV% = 15, Simulations = 106, N = 36, w0=0.25
Scaled ABE + Point EstimateAverage BE
Source: Sam H. Haidar: AAPS/FDA Workshop 5/22/2077, Rockville, MD
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1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7
Geometric Mean Ratio
0
20
40
60
80
100
Perc
en
t o
f S
tud
ies P
assin
gAverage vs. Scaled Average Bioequivalence
CV% = 30, Simulations = 106, N = 36, w0=0.25
Scaled ABE + Point EstimateAverage BE
Source: Sam H. Haidar: AAPS/FDA Workshop 5/22/2077, Rockville, MD
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1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7
Geometric Mean Ratio
0
20
40
60
80
100
Perc
en
t o
f S
tud
ies P
assin
gAverage vs. Scaled Average Bioequivalence
CV% = 60, Simulations = 106, N = 36, w0=0.25
Scaled ABE + Point EstimateAverage BE
Source: Sam H. Haidar: AAPS/FDA Workshop 5/22/2077, Rockville, MD
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Impact of Point Estimate Constraint
• Lower variability (30% CV)
• Higher variability (60% CV)
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1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7
Geometric Mean Ratio
0
20
40
60
80
100
Perc
en
t o
f S
tud
ies P
assin
gAverage vs. Scaled Average Bioequivalence
CV% = 30, Simulations = 106, N = 36, w0=0.25
Scaled ABEPoint EstimateScaled ABE + Point EstimateAverage BE
Source: Sam H. Haidar: AAPS/FDA Workshop 5/22/2077, Rockville, MD
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1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7
Geometric Mean Ratio
0
20
40
60
80
100
Perc
en
t o
f S
tud
ies P
assin
gAverage vs. Scaled Average Bioequivalence
CV% = 60, Simulations = 106, N = 36, w0=0.25
Scaled ABEPoint EstimateScaled ABE + Point EstimateAverage BE
Source: Sam H. Haidar: AAPS/FDA Workshop 5/22/2077, Rockville, MD
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Impact of σW0
σW0 = 0.2
σW0 = 0.25
σW0 = 0.294
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1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7
Geometric Mean Ratio
0
20
40
60
80
100
Perc
en
t o
f S
tud
ies P
assin
gImpact of w0 on the Power
CV% = 30, Simulations = 106, N = 36
Average BEScaled + Point Estimate (w0 = 0.2)Scaled + Point Estimate (w0 = 0.25)Scaled + Point Estimate (w0 = 0.294)
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1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7
Geometric Mean Ratio
0
20
40
60
80
100
Perc
en
t o
f S
tud
ies P
assin
gImpact of w0 on the Power
CV% = 60, Simulations = 106, N = 36
Average BEScaled + Point Estimate (w0 = 0.2)Scaled + Point Estimate (w0 = 0.25)Scaled + Point Estimate (w0 = 0.294)
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1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7
Geometric Mean Ratio
0
20
40
60
80
100
Perc
en
t o
f S
tud
ies P
assin
gAverage vs. Scaled Average Bioequivalence
CV% = 60, Simulations = 106, N = 36 vs. 24, w0=0.25
Scaled ABE + Point Estimate (N = 24)Average BE (N = 24)Scaled ABE + Point Estimate (N = 36)Average BE (N = 36)
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Impact of Different Point Estimate Constraints
• Point estimate constraint = ±15%
• Point estimate constraint = ± 20%
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1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7
Geometric Mean Ratio
0
20
40
60
80
100
Perc
en
t o
f S
tud
ies P
assin
gScaled + Point Estimate constraint of 15%
Simulations = 1000000, N = 24
CV 30%CV 40%CV 50%CV 60%
Source: Sam H. Haidar: AAPS/FDA Workshop 5/22/2077, Rockville, MD
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1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7
Geometric Mean Ratio
0
20
40
60
80
100
Perc
en
t o
f S
tud
ies P
assin
gScaled + Point Estimate constraint of 20%
Simulations = 1000000, N = 24
CV 30%CV 40%CV 50%CV 60%
Source: Sam H. Haidar: AAPS/FDA Workshop 5/22/2077, Rockville, MD
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Summary
Partial replicate, 3-way crossover design appears to work well
A point estimate constraint has little impact at lower variability (~30%); more significant effect at greater variability (~60%)
A σW0 = 0.25 demonstrates a good balance between a conservative approach, and a practical one
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Conclusion
Scaled ABE presents a reasonable option for evaluating BE of highly variable drugs
Practical value, reduction in sample size: Potentially decreasing cost and unnecessary human testing (without increase in patient risk)
Use of point estimate constraint addresses concerns that products with large GMR differences may be judged bioequivalent
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FDA Proposal*: Scaled Average BE for HVA Drugs
• Three-period, partial replicate design– Reference product (R) is administered twice– Test product (T) is administered once– Sequences = RTR, TRR, RRT
• Sample size: Determined by sponsor (adequate power)– minimum is 24 subjects
* Currently under evaluation
Source: Sam H. Haidar: AAPS/FDA Workshop 5/22/2077, Rockville, MD
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FDA Proposal- continued
• BE criteria scaled to reference variability (Cmax & AUC)
– Where σw0 = 0.25
• The point estimate (test/reference geometric mean ratio) must fall within [0.80-1.25]
• Both conditions must be passed by the test product to conclude BE to the reference product
wr
w0
σσ
0.223EXP lower upper, limits, BE
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Use of reference average BE for HVD
• BE criteria scaled to reference variability• 90% upper confidence bound for:
Ho: (µT-µR)2 – θ σ2wr must be ≤ 0
Where θ = scaled average BE limitand
θ = (ln Δ)2/ σ2wo
Where σwo = 0.25
Use a point estimate constraintBoth Cmax and AUC must meet criteria
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Advantages of scaled BEreference scaled
• Test product will benefit if:– T variability < R variability
• The test product will not benefit if:– T variability > R variability
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Concerns with Proposed Approach
• Firms will conduct a replicate design study and submit results to FDA– If within subject variability ≥ 30%, FDA will use the reference-
scaled average BE approach– If within subject variability ≤ 30%, FDA will use the unscaled
average BE approach
• What if the drug is characterized as a borderline HV drug?– FDA simulations showed that study outcome will be the same
whether the scaled or unscaled approach is used
• Scaling can allow AUC and Cmax GMR to be unacceptably high or low– Acceptance criteria will include a point estimate constraint
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Concerns with Proposed Approach
• What if high variability results from formulations problems or poor study conduct?– If T variability > R variability, no benefit in
using scaled approach– The burden is on the applicant to convince
FDA that product is a HVD
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1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7
Geometric Mean Ratio
0
20
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Perc
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gImpact Of Test CV% On Study Power Using Scaled Average BE
CVRef% = 30, Simulations = 106, N = 24, w0=0.25
CVTest 30%CVTest 40%CVTest 50%CVTest 60%
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1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7
Geometric Mean Ratio
0
20
40
60
80
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Perc
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gImpact Of Small Sample Size On Study Power Using Scaled Average BE
CVRef% = 30, CVTest% = 30, Simulations = 106, w0=0.25
n = 24n = 21n = 18n = 15n = 12
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спасибо
Thank you