In Silico prediction of cytochrome P450-drug interaction: QSARs for CYP3a4 and CYP2C9

Serena Nembri, Francesca Grisoni, Viviana Consonni, Roberto Todeschini

Research output: Contribution to journalArticleAcademicpeer-review

38 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number914
JournalInternational Journal of Molecular Sciences
Volume17
Issue number6
DOIs
Publication statusPublished - 9 Jun 2016
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2016 by the authors; licensee MDPI, Basel, Switzerland.

Keywords

  • ADMET
  • CYP2C9
  • CYP3A4
  • Cytochrome P450
  • In silico
  • QSAR

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