Pre-qualification Program: Priority Medicines Interchangeability of Multi Source Drug Products SALOMON STAVCHANSKY, PH.D. ALCON CENTENNIAL PROFESSOR OF

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

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

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?

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

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

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

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

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 !

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

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


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

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


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

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

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

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

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

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

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)

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

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)

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

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

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

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.

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

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

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


Impact of Within Subject Variability

• 15% CV• 30% CV• 60% CV

1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7

Geometric Mean Ratio

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gAverage vs. Scaled Average Bioequivalence

CV% = 15, Simulations = 106, N = 36, w0=0.25

Scaled ABE + Point EstimateAverage BE


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Impact of Point Estimate Constraint

• Lower variability (30% CV)

• Higher variability (60% CV)

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Scaled ABEPoint EstimateScaled ABE + Point EstimateAverage BE


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Scaled ABEPoint EstimateScaled ABE + Point EstimateAverage BE


Impact of σW0

σW0 = 0.2

σW0 = 0.25

σW0 = 0.294

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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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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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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)

Impact of Different Point Estimate Constraints

• Point estimate constraint = ±15%

• Point estimate constraint = ± 20%

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gScaled + Point Estimate constraint of 15%

Simulations = 1000000, N = 24

CV 30%CV 40%CV 50%CV 60%


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gScaled + Point Estimate constraint of 20%

Simulations = 1000000, N = 24

CV 30%CV 40%CV 50%CV 60%


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

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

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


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

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

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

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

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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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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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

спасибо

Thank you

Documents

Pre-qualification Program: Priority Medicines Interchangeability of Multi Source Drug Products SALOMON STAVCHANSKY, PH.D. ALCON CENTENNIAL PROFESSOR OF