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An individual bioequivalence criterion: regulatory considerations
M L Chen1, R Patnaik, W W Hauck
1Division of Pharmaceutical Evaluation II (HFD-870), Office of Clinical Pharmacology and Biopharmaceutics, Center for Drug Evaluation and Research, 5600 Fishers Lane, Rockville, MD 20857, USA.
Statistics in Medicine
|October 18, 2000
Summary
The FDA is exploring individual bioequivalence to ensure drug switchability. This approach, unlike average bioequivalence, considers within-subject variability and subject-by-formulation interactions for better therapeutic outcomes.
Area of Science:
- Pharmacokinetics and Drug Development
- Regulatory Science
- Biostatistics
Background:
- Traditional average bioequivalence (ABE) may not adequately assess drug product interchangeability.
- Growing concerns exist regarding ABE's limitations in evaluating formulation comparability.
- Population and individual bioequivalence concepts have been developed as alternatives.
Purpose of the Study:
- To discuss the shift towards individual bioequivalence (IBE) for evaluating drug product comparability.
- To highlight the FDA's preliminary draft guidance on IBE.
- To emphasize the importance of switchability for IBE.
Main Methods:
- Exploration of population and individual bioequivalence statistical methods.
- Analysis of the FDA's draft guidance on in vivo bioequivalence studies.
- Comparison of IBE with the current average bioequivalence procedure.
Main Results:
- IBE focuses on switchability, ensuring interchangeable use of drug products.
- IBE assesses within-subject variability and subject-by-formulation interaction, unlike ABE.
- Proposed IBE criteria offer flexibility based on therapeutic windows and drug variability.
Conclusions:
- Individual bioequivalence offers a more nuanced approach to drug comparability than average bioequivalence.
- IBE criteria can reward less variable drug products and accommodate highly variable or narrow therapeutic range drugs.
- The FDA is actively considering public feedback on IBE, particularly regarding interaction terms and statistical methods.