Towards Virtual Bioequivalence Studies for Oral Dosage Forms Containing Poorly Water-Soluble Drugs: A Physiologically Based Biopharmaceutics Modeling (PBBM) Approach

The objective of the present study was to develop a physiologically based biopharmaceutics (PBBM) approach to predict the bioequivalence of dosage forms containing poorly soluble drugs. Aripiprazole and enzalutamide were used as model drugs. Variations in the gastrointestinal (GI) physiological parameters of fasted humans were taken into consideration in in vitro biorelevant dissolution testing and in an in silico PBBM simulations. To estimate bioequivalence between dosage forms, the inter-individual variabilities in their performance in virtual human subjects were predicted from the in vitro studies and variability in e.g. gastric emptying and fluid volume in the stomach was also taken into account. Formulations with different in vitro dissolution performance, a solution and a tablet formulation, were used in order to evaluate the accuracy of bioequivalence prediction using the PBBM approach. The bioequivalence parameters, i.e. geometric mean ratio and 90% confidence interval, for both drugs were predicted well in the virtual studies. In order to achieve even more precise predictions, it will be important to continue characterizing GI physiological parameters, along with their variabilities, on both an inter-subject and inter-occasion basis. (C) 2021 American Pharmacists Association. Published by Elsevier Inc. All rights reserved.

Automated summary

The present study aimed to develop a physiologically based biopharmaceutics approach to predict the bioequivalence of dosage forms containing poorly soluble drugs. The variations in gastrointestinal physiological parameters and inter-individual variabilities were considered in in vitro dissolution testing and in silico simulations. The bioequivalence between different formulations was accurately predicted using the proposed approach. Further characterization of gastrointestinal physiological parameters and their variabilities is important for more precise predictions.