Use of clearance concepts and modeling techniques in the prediction of metabolic drug–drug interactions

Quantitative prediction of the in vivo drug–drug interactions (DDIs) caused by metabolic inhibition, one of the most common DDI mechanisms in clinical practice, has long been challenged. The DDI-induced increase in the area under the plasma concentration–time curve of a substrate drug can now be predicted with a certain degree of accuracy based on the inhibition parameters obtained in in vitro studies together with information on the pharmacokinetic properties of both the substrate and inhibitor. Here we argue that physiologically based pharmacokinetic modeling facilitates more precise prediction of the DDI-induced change in substrate exposure and is also expected to assist in prediction of recently recognized DDIs involving drug transporters. Quantitative prediction of DDIs involving both metabolism and transport would provide valuable information for increased efficiency in drug development and avoidance of toxic interactions in clinical practice.