In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9.
Cytochromes P450 (CYP) are the main actors in the oxidation of xenobiotics and play a crucial role in drug safety, persistence, bioactivation, and drug-drug/food-drug interaction. This work aims to develop Quantitative Structure-Activity Relationship (QSAR) models to predict the drug interaction with two of the most important CYP isoforms, namely 2C9 and 3A4. The presented models are calibrated on 9122 drug-like compounds, using three different modelling approaches and two types of molecular description (classical molecular descriptors and binary fingerprints). For each isoform, three classification models are presented, based on a different approach and with different advantages: (1) a very simple and interpretable classification tree; (2) a local (k-Nearest Neighbor) model based classical descriptors and; (3) a model based on a recently proposed local classifier (N-Nearest Neighbor) on binary fingerprints. The salient features of the work are (1) the thorough model validation and the applicability domain assessment; (2) the descriptor interpretation, which highlighted the crucial aspects of P450-drug interaction; and (3) the consensus aggregation of models, which largely increased the prediction accuracy.
['Nembri|Serena|S|', 'Grisoni|Francesca|F|', 'Consonni|Viviana|V|', 'Todeschini|Roberto|R|']
D000818:Animals D003198:Computer Simulation D065729:Cytochrome P-450 CYP2C9 D065688:Cytochrome P-450 CYP2C9 Inhibitors D051544:Cytochrome P-450 CYP3A D065692:Cytochrome P-450 CYP3A Inhibitors D006801:Humans D011485:Protein Binding D021281:Quantitative Structure-Activity Relationship
in silico QSAR CYP3A4 CYP2C9 cytochrome P450 ADMET