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QSAR Modelling of CYP3A4 Inhibition as a Screening Tool in the Context of DrugDrug Interaction Studies
Author(s) -
Hamon Véronique,
Horvath Dragos,
Gaudin Cédric,
Desrivot Julie,
Junges Céline,
Arrault Alban,
Bertrand Marc,
Vayer Philippe
Publication year - 2012
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201200004
Subject(s) - quantitative structure–activity relationship , in silico , pharmacophore , pubchem , drugbank , context (archaeology) , drug drug interaction , computational biology , cheminformatics , cyp3a4 , drug , drug discovery , computer science , ranking (information retrieval) , drug development , molecular descriptor , chemistry , data mining , machine learning , cytochrome p450 , pharmacology , stereochemistry , biology , computational chemistry , biochemistry , paleontology , metabolism , gene
Drugdrug interaction potential (DDI), especially cytochrome P450 (CYP) 3A4 inhibition potential, is one of the most important parameters to be optimized before preclinical and clinical pharmaceutical development as regard to the number of marketed drug metabolized mainly by this CYP and potentially co‐administered with the future drug. The present study aims to develop in silico models for CYP3A4 inhibition prediction to help medicinal chemists during the discovery phase and even before the synthesis of new chemical entities (NCEs), focusing on NCEs devoid of any inhibitory potential toward this CYP. In order to find a relevant relationship between CYP3A4 inhibition and chemical features of the screened compounds, we applied a genetic‐algorithm‐based QSAR exploratory tool SQS (Stochastic QSAR Sampler) in combination with different description approaches comprising alignment‐independent Volsurf descriptors, ISIDA fragments and Topological Fuzzy Pharmacophore Triplets. The experimental data used to build models were extracted from an in‐house database. We derived a model with good prediction ability that was confirmed on both newly synthesized compound and public dataset retrieved from Pubchem database. This model is a promising efficient tool for filtering out potentially problematic compounds.