Generalized proteochemometric model of multiple cytochrome P450 enzymes and their inhibitors
2008 (English)In: Journal of chemical information and modeling, ISSN 1549-9596, Vol. 48, no 9, 1840-1850 p.Article in journal (Refereed) Published
Cytochrome P450 enzymes are a superfamily of heme-containing enzymes responsible for the oxidation of structurally diverse chemical Compounds. Inhibition of CYP enzymes is probably the most common mechanism underlying acute drug toxicity, loss of therapeutic drug efficacy, and drug-drug interactions. The presence of polymorphic genetic variants of CYPs among the population makes it difficult to foresee undesired effects of drugs and is a common cause of drug candidate failure. Computational models that can predict early drug failures due to the inhibition of CYP isoforms can substantially reduce the cost of drug development. Although several computational models for CYP inhibition have been developed recently, all were constructed for one CYP isoform at a time, thus limiting their use for comprehensive analysis and generalizations to other CYP isoforms and polymorphisms. Here we report a novel approach based on the principles of proteochemometrics for the generalized concomitant modeling of multiple CYP isoforms and their inhibitors. We created a predictive and statistically valid proteochemometric model for CYP enzymes by combining data from a large number of publicly available reports that describe the interactions of 14 CYP enzyme subtypes and 375 structurally diverse inhibitors. Our results demonstrate that Our model is capable of predicting the potential of new drug candidates to inhibit Multiple CYP enzymes. Analysis of the CYP model also revealed molecular properties of CYP enzymes and xenobiotics that are important for CYP inhibition. This approach may aid in the selection of novel drug, candidates that are unlikely to inhibit multiple CYP subtypes.
Place, publisher, year, edition, pages
2008. Vol. 48, no 9, 1840-1850 p.
IdentifiersURN: urn:nbn:se:uu:diva-97335DOI: 10.1021/ci8000953ISI: 000259398500011PubMedID: 18693719OAI: oai:DiVA.org:uu-97335DiVA: diva2:172222