In vitro data are used to predict the magnitude of in vivo metabolism-based DDIs in the clinic. No animal in vivo model is entirely suitable for assessing DDI risk in humans due to many factors, including differences in enzymes and disposition of the compound. Potential drug interactions are assessed by taking into consideration the drug as a victim or a perpetrator.
There are limitations to quantitatively predict in vivo DDIs due to factors such as the involvement of transporters and uncertainty in the concentrations of the substrate and inhibitor at the active site of the enzyme. However, in addition to DDI prediction, it is possible to use in vitro data to rank order the isoforms according to inhibitory potency. This allows for clinical DDI studies to be performed first on the most potently inhibited enzyme, the outcome of which then informs further studies. This approach may prevent potentially unnecessary clinical drug interaction studies if no clinical inhibition is observed in the first study.
In the following sections, we discuss the static prediction models used for competitive and mechanism-based inhibitors and inductors. Dynamic models are currently available using Simcyp® software.
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