Evaluation of a Scaling Approach for Highly Variable Drugs Sam H. Haidar, Ph.D., R.Ph. Office of Generic Drugs Advisory Committee for Pharmaceutical Sciences.

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Presentation transcript:

Evaluation of a Scaling Approach for Highly Variable Drugs Sam H. Haidar, Ph.D., R.Ph. Office of Generic Drugs Advisory Committee for Pharmaceutical Sciences October 6, 2006

Outline Background Simulations-Based Research Project Results and Conclusion

Background 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

Research Project Highly Variable Drugs (HVD) working group evaluated different scaling approaches and study designs to test. Outcome: Research project using: –Scaled average bioequivalence, based on within subject variability of reference* *

Objective Determine the impact of scaled average bioequivalence on the power (percent of studies passing) at different levels of within subject variability (CV%)

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 Statistical Analysis: Modified Hyslop model* Number of simulations: 1 million (10 6 )/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: Hyslop’s model was modified by Donald Schuirmann

Methods Variables tested: Impact of increasing within subject variability Use of point estimate constraint (80-125) σ w0 : 0.2 vs vs Sample size: 24 vs. 36

Results

Impact of Within Subject Variability 15% CV 30% CV 60% CV

Impact of Point Estimate Constraint Lower variability (30% CV) Higher variability (60% CV)

Impact of σ W0 σ W0 = 0.2 σ W0 = 0.25 σ W0 = 0.294

18

19

Sample Size N = 24 N = 36

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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 σ W0 = 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: 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

Acknowledgments Barbara Davit (Co-Chair) Lawrence Yu Donald Schuirmann Fairouz Makhlouf Dale Conner Mei-Ling Chen Devvrat Patel Lai Ming Lee Highly Variable Drugs Working Group:

Acknowledgments Robert Lionberger Qian Li Sarah Marston Other Contributors: