Probing Small-Molecule Binding to Cytochrome P450 2D6 and 2C9: An In Silico Protocol for Generating Toxicity Alerts

• Published in: ChemMedChem Vol. 5; no. 12; pp. 2088 - 2101
• Main Authors: Rossato, Gianluca, Ernst, Beat, Smiesko, Martin, Spreafico, Morena, Vedani, Angelo
• Format: Journal Article
• Language: English
• Published: Weinheim WILEY-VCH Verlag 03.12.2010; WILEY‐VCH Verlag
• Subjects: Aryl Hydrocarbon Hydroxylases - chemistry; Aryl Hydrocarbon Hydroxylases - metabolism; Binding Sites; computational chemistry; Cytochrome P-450 CYP2C9; Cytochrome P-450 CYP2D6 - chemistry; Cytochrome P-450 CYP2D6 - metabolism; cytochromes; enzymes; Humans; Molecular Dynamics Simulation; mQSAR; pharmacophore alignment; Protein Binding; Protein Structure, Tertiary; Quantitative Structure-Activity Relationship; Small Molecule Libraries - chemical synthesis; Small Molecule Libraries - chemistry; Small Molecule Libraries - toxicity
• ISSN: 1860-7179; 1860-7187
• DOI: 10.1002/cmdc.201000358

Drug metabolism, toxicity, and their interaction profiles are major issues in the drug‐discovery and lead‐optimization processes. The cytochromes P450 (CYPs) 2D6 and 2C9 are enzymes involved in the oxidative metabolism of a majority of marketed drugs. Therefore, the prediction of the binding affinity towards CYP2D6 and CYP2C9 would be beneficial for identifying cytochrome‐mediated adverse effects triggered by drugs or chemicals (e.g., toxic reactions, drug–drug, and food–drug interactions). By identifying the binding mode by using pharmacophore prealignment, automated flexible docking, and by quantifying the binding affinity by multidimensional QSAR (mQSAR), we validated a model family of 56 compounds (46 training, 10 test) and 85 compounds (68 training, 17 test) for CYP2D6 and CYP2C9, respectively. The correlation with the experimental data (cross‐validated r2=0.811 for CYP2D6 and 0.687 for CYP2C9) suggests that our approach is suited for predicting the binding affinity of compounds towards CYP2D6 and CYP2C9. The models were challenged by Y‐scrambling and by testing an external dataset of binding compounds (15 compounds for CYP2D6 and 40 for CYP2C9). To assess the probability of false‐positive predictions, datasets of nonbinders (64 compounds for CYP2D6 and 56 for CYP2C9) were tested by using the same protocol. The two validated mQSAR models were subsequently added to the VirtualToxLab (VTL, http://www.virtualtoxlab.org). To bind or not to bind: A multidimensional QSAR (mQSAR) study has been performed on cytochrome P450 (CYP) CYP2D6 and CYP2C9. To generate the two models, we used a mixed approach based on pharmacophore prealignment, automated flexible docking, and multidimensional QSAR (mQSAR). The models were used to quantify binding affinities for external datasets of binders and nonbinders in compounds (to test the probability of false‐positive predictions).