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, S., Grisoni, F., Consonni, V., Todeschini, R. (2016). In Silico prediction of cytochrome P450-drug interaction: QSARs for CYP3a4 and CYP2C9. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 17(6), 1-19 [10.3390/ijms17060914].

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

GRISONI, FRANCESCA;CONSONNI, VIVIANA;TODESCHINI, ROBERTO
2016

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.
Articolo in rivista - Articolo scientifico
cytochrome P450; QSAR; CYP2C9; CYP3A4; in silico; ADMET
English
2016
17
6
1
19
914
partially_open
Nembri, S., Grisoni, F., Consonni, V., Todeschini, R. (2016). In Silico prediction of cytochrome P450-drug interaction: QSARs for CYP3a4 and CYP2C9. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 17(6), 1-19 [10.3390/ijms17060914].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/115629
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