J Chem Inf Model - Predictive models for cytochrome p450 isozymes based on quantitative high throughput screening data.

Tópicos

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Resumo

The human cytochrome P450 (CYP450) isozymes are the most important enzymes in the body to metabolize many endogenous and exogenous substances including environmental toxins and therapeutic drugs. Any unnecessary interactions between a small molecule and CYP450 isozymes may raise a potential to disarm the integrity of the protection. Accurately predicting the potential interactions between a small molecule and CYP450 isozymes is highly desirable for assessing the metabolic stability and toxicity of the molecule. The National Institutes of Health Chemical Genomics Center (NCGC) has screened a collection of over 17,000 compounds against the five major isozymes of CYP450 (1A2, 2C9, 2C19, 2D6, and 3A4) in a quantitative high throughput screening (qHTS) format. In this study, we developed support vector classification (SVC) models for these five isozymes using a set of customized generic atom types. The CYP450 data sets were randomly split into equal-sized training and test sets. The optimized SVC models exhibited high predictive power against the test sets for all five CYP450 isozymes with accuracies of 0.93, 0.89, 0.89, 0.85, and 0.87 for 1A2, 2C9, 2C19, 2D6, and 3A4, respectively, as measured by the area under the receiver operating characteristic (ROC) curves. The important atom types and features extracted from the five models are consistent with the structural preferences for different CYP450 substrates reported in the literature. We also identified novel features with significant discerning power to separate CYP450 actives from inactives. These models can be useful in prioritizing compounds in a drug discovery pipeline or recognizing the toxic potential of environmental chemicals.

Resumo Limpo

human cytochrom p cyp isozym import enzym bodi metabol mani endogen exogen substanc includ environment toxin therapeut drug unnecessari interact small molecul cyp isozym may rais potenti disarm integr protect accur predict potenti interact small molecul cyp isozym high desir assess metabol stabil toxic molecul nation institut health chemic genom center ncgc screen collect compound five major isozym cyp c c d quantit high throughput screen qhts format studi develop support vector classif svc model five isozym use set custom generic atom type cyp data set random split equals train test set optim svc model exhibit high predict power test set five cyp isozym accuraci c c d respect measur area receiv oper characterist roc curv import atom type featur extract five model consist structur prefer differ cyp substrat report literatur also identifi novel featur signific discern power separ cyp activ inact model can use priorit compound drug discoveri pipelin recogn toxic potenti environment chemic

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