In Silico Prediction of Cytochrome P450 Inhibitors

Patients often receive several medications at the same time, and if the drugs involved compete for the same enzymes to be metabolized, it can lead to undesired effects with the risk of fatal results. Therefore, early knowledge about the cytochrome P450 (CYP) interaction potential of a drug candidate is central and in silico tools can provide such information even on virtual structures. Most of the in silico CYP information in the literature is on substrates and is based on molecular and protein modeling. However, in early screening information of CYP substrates is rarely available and sometimes only a single concentration is used in screening assays. Recently, in silico CYP modeling applying statistical tools has appeared in the literature and the aim of this review is to give an overview of published in silico prediction studies of CYP inhibition for four of the clinically most important isotypes, namely: CYP1A2, CYP2C9, CYP2D6, and CYP3A4. Furthermore, in the review, we discuss inhibition data, different descriptors and statistical methods applied for in silico prediction of CYP inhibition, and we point to promising approaches in the development of accurate in silico prediction tools of CYP inhibitors.