Case studies, practical issues and observations on population and individual bioequivalence.

Area of Science:

• Pharmacokinetics and Drug Development
• Regulatory Science
• Biostatistics

Background:

• The FDA is proposing new bioequivalence criteria: population bioequivalence (PBE) and individual bioequivalence (IBE), replacing the current average bioequivalence (ABE) standard.
• Limited data exists on the performance of PBE and IBE, prompting expert discussion.
• Bioequivalence studies are crucial for generic drug approval and ensuring therapeutic equivalence.

Purpose of the Study:

• To retrospectively analyze existing bioequivalence data using ABE, PBE, and IBE criteria.
• To evaluate the behavior and implications of the proposed PBE and IBE methods compared to ABE.
• To identify potential challenges and impacts of implementing PBE and IBE, particularly for highly variable drugs.

Main Methods:

• Retrospective analysis of 22 data sets from 15 replicate cross-over bioequivalence studies (n=12-74).
• Analysis of AUC and Cmax parameters using ABE, PBE, and IBE statistical methods.
• Examination of key parameters and their interrelationships, focusing on the subject by formulation term in IBE.

Main Results:

• For AUC, PBE and IBE showed higher pass rates than ABE, with most ABE failures passing PBE.
• For Cmax, results were more variable: some ABE-passing datasets failed PBE/IBE, and some ABE-failing datasets passed PBE/IBE.
• A notable finding was that five datasets passed both ABE and PBE but failed IBE, highlighting IBE's stricter nature.

Conclusions:

• PBE and IBE criteria exhibit different behaviors compared to ABE, with varying pass rates for AUC and Cmax.
• Further studies and simulations are recommended before the full implementation of PBE and IBE.
• Understanding the impact on sample size and the behavior of the subject by formulation term in IBE is critical for practical application.