Machine learning driven bioequivalence risk assessment at an early stage of generic drug development.

Background: Bioequivalence risk assessment as an extension of quality risk management lacks examples of quantitative approaches to risk assessment at an early stage of generic drug development. The aim of our study was to develop a model-based approach for bioequivalence risk assessment that uses pharmacokinetic and physicochemical characteristics of drugs as predictors and would standardize the first step of risk assessment.

Methods: The Sandoz in-house bioequivalence database of 128 bioequivalence studies with poorly soluble drugs (23.