Cytochrome P450 (CYP) is a family of heme-containing enzymes, which are responsible for the metabolism of a variety of drugs. Inhibition of CYP is one of the key factors underlying drug toxicity, loss of therapeutic efficacy, and drug-drug interactions. Computational strategies aimed at generating models for the estimation and prediction of CYP modulation are of great importance. In the present work, we have organized data sets of structurally diverse molecules with known inhibition of CYP3A4 (IC50 values ranging from 0.3 to 450.000 nM), and used the data to create predictive QSAR models, employing a specialized fragment-based method (HQSAR). Statistically significant models were obtained showing high predictive ability for molecules not included in the training sets (external validation). The final consensus predictive QSAR models should be useful for the design of new NCEs with desirable properties. The results of modeling these data sets will be presented and discussed.

Multiple in silico models for the inhibition of cytochrome P450 (CYP3A4)

243rd National Meeting (2012)

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