Prediction of pharmacokinetics and drug-drug interactions from in vitro metabolism data

Abstract

There is great interest within the pharmaceutical industry in predicting the in vivo pharmacokinetics (PKs) and metabolism-based drug-drug interactions (DDIs) of compounds from their in vitro metabolism data. Metabolism-based DDIs are largely due to changes in levels of drug-metabolizing enzymes caused by one drug, leading to changes in the PK parameters (mainly clearance) of another. The search for alternative approaches to time-consuming and costly clinical PK drug interaction studies for predicting human DDIs, has been ongoing for decades. In vitro enzyme-mediated biotransformation reactions provide a foundation for predictions that relate PK concepts to enzyme kinetics. This review discusses the principles, assumptions, tools and approaches to in vitro/in vivo prediction, especially in the context of hepatic clearance (the most important PK parameter) and its prediction from in vitro data. Enzyme inhibition is a common cause of DDIs and involves various mechanisms (eg, reversible and mechanism-based inhibition). The models and equations used for predicting DDIs for different types of inhibitor (ie, competitive, partial competitive, non-competitive, partial non-competitive and mixed-type reversible inhibitors, and mechanism-based inhibitors) are extensively presented. Although the methods of prediction are numerous, there remain a number of unresolved factors that may affect the accuracy of the prediction. These factors are also discussed to provide a caution to researchers performing prediction studies.