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Shared IVIVR for Five Commercial Enabling Formulations Using the BiPHa+ Biphasic Dissolution Assay.
Alexander Denninger1, Ulrich Westedt2, Karl G Wagner1
1Department of Pharmaceutical Technology, University of Bonn, Gerhard-Domagk-Straße 3, 53121 Bonn, Germany.
The BiPHa+ biphasic dissolution assay accurately predicts in vivo drug absorption. This assay, using a single parameter set, correlates in vitro partitioning with human pharmacokinetic data for enhanced drug development.
Area of Science:
- Pharmaceutical Sciences
- Drug Delivery and Formulation
- Pharmacokinetics
Background:
- Assessing in vivo drug absorption is crucial for formulation development.
- Existing in vitro methods may not fully capture complex absorption processes.
- The BiPHa+ biphasic dissolution assay aims to improve in vitro-in vivo correlation.
Purpose of the Study:
- To confirm the in vivo relevance of the BiPHa+ biphasic dissolution assay.
- To evaluate the assay's performance with diverse commercial drug formulations.
- To establish a predictive in vitro/in vivo relationship for drug absorption.
Main Methods:
- Utilized the BiPHa+ assay to evaluate five commercial drug products under fasted conditions.
- Determined maximum drug concentration in the organic phase (Cmax) as an in vitro dose metric.
- Correlated in vitro partitioning profiles with human pharmacokinetic data (fraction absorbed).
- Employed convolutional modeling to predict human in vivo plasma profiles from in vitro kinetics.
Main Results:
- An in vitro dose of 10 mg (15-25 µmol) yielded the highest correlation between in vitro partitioning and in vivo fraction absorbed.
- The BiPHa+ assay demonstrated biorelevance across various formulation types.
- Predicted human pharmacokinetic profiles closely matched observed in vivo performance.
Conclusions:
- The BiPHa+ biphasic dissolution assay is a biorelevant tool for predicting in vivo drug absorption.
- A single set of assay parameters is sufficient for establishing in vitro/in vivo relationships.
- The assay shows significant predictive power for drug product performance.
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