Next Article in Journal
Neuropathy and Diabetic Foot Syndrome
Next Article in Special Issue
Genome-Wide Discriminatory Information Patterns of Cytosine DNA Methylation
Previous Article in Journal
Studies to Prevent Degradation of Recombinant Fc-Fusion Protein Expressed in Mammalian Cell Line and Protein Characterization
Previous Article in Special Issue
Development of an in Silico Model of DPPH• Free Radical Scavenging Capacity: Prediction of Antioxidant Activity of Coumarin Type Compounds
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Int. J. Mol. Sci. 2016, 17(6), 914;

In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9

Department of Earth and Environmental Sciences, University of Milano-Bicocca, della Scienza 1, 20126 Milano, Italy
These authors contributed equally to the work.
Author to whom correspondence should be addressed.
Academic Editor: Jesus Vicente De Julián Ortiz
Received: 16 May 2016 / Revised: 1 June 2016 / Accepted: 6 June 2016 / Published: 9 June 2016
Full-Text   |   PDF [8849 KB, uploaded 9 June 2016]   |  


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. View Full-Text
Keywords: cytochrome P450; QSAR; CYP2C9; CYP3A4; in silico; ADMET cytochrome P450; QSAR; CYP2C9; CYP3A4; in silico; ADMET

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material


Share & Cite This Article

MDPI and ACS Style

Nembri, S.; Grisoni, F.; Consonni, V.; Todeschini, R. In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9. Int. J. Mol. Sci. 2016, 17, 914.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top