Evaluation of a Scaling Approach for Highly Variable Drugs

1
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

2
Outline

• Background
• Simulations-Based Research Project
• Results and Conclusion

3
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
4
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

5
Objective

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

6
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

7
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 192885-2897.
Hyslops model was modified by Donald
Schuirmann
8
Methods

• Variables tested
• Impact of increasing within subject variability
• Use of point estimate constraint (80-125)
• sw0 0.2 vs. 0.25 vs. 0.294
• Sample size 24 vs. 36

9
Results

10
Impact of Within Subject Variability

• 15 CV
• 30 CV
• 60 CV

11
(No Transcript)
12
(No Transcript)
13
(No Transcript)
14
Impact of Point Estimate Constraint

• Lower variability (30 CV)
• Higher variability (60 CV)

15
(No Transcript)
16
(No Transcript)
17
Impact of sW0

• sW0 0.2
• sW0 0.25
• sW0 0.294

18
(No Transcript)
19
(No Transcript)
20
Sample Size

• N 24
• N 36

21
(No Transcript)
22
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 sW0 0.25 demonstrates a good balance between
  a conservative approach, and a practical one

23
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

24
Acknowledgments
Highly Variable Drugs Working Group

• Barbara Davit (Co-Chair)
• Lawrence Yu
• Donald Schuirmann
• Fairouz Makhlouf

• Dale Conner
• Mei-Ling Chen
• Devvrat Patel
• Lai Ming Lee

25
Acknowledgments
Other Contributors

• Robert Lionberger
• Qian Li
• Sarah Marston