# Presentation: 759226

## 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 (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

## 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

## Results

## Impact of Within Subject Variability

- 15% CV
- 30% CV
- 60% CV

## Slide 11

## Slide 12

## Slide 13

## Impact of Point Estimate Constraint

- Lower variability (30% CV)
- Higher variability (60% CV)

## Slide 15

## Slide 16

## Impact of σW0 

- σW0 = 0.2
- σW0 = 0.25
- σW0 = 0.294

## Slide 18

## Slide 19

## Sample Size

- N = 24
- N = 36

## Slide 21

## 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: 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:_