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Int. J. Mol. Sci. 2016, 17(10), 1686; doi:10.3390/ijms17101686

Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System

1
Key Laboratory of Traditional Chinese Medicine-Information Engineer of State Administration of Traditional Chinese Medicine, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100102, China
2
College of Chinese Medicine, Hebei University, Baoding 071002, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Mihai V. Putz and Christo Z. Christov
Received: 28 June 2016 / Revised: 22 September 2016 / Accepted: 30 September 2016 / Published: 9 October 2016
(This article belongs to the Special Issue Chemical Bond and Bonding 2016)
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Abstract

During the past decades, there have been continuous attempts in the prediction of metabolism mediated by cytochrome P450s (CYP450s) 3A4, 2D6, and 2C9. However, it has indeed remained a huge challenge to accurately predict the metabolism of xenobiotics mediated by these enzymes. To address this issue, microsomal metabolic reaction system (MMRS)—a novel concept, which integrates information about site of metabolism (SOM) and enzyme—was introduced. By incorporating the use of multiple feature selection (FS) techniques (ChiSquared (CHI), InfoGain (IG), GainRatio (GR), Relief) and hybrid classification procedures (Kstar, Bayes (BN), K-nearest neighbours (IBK), C4.5 decision tree (J48), RandomForest (RF), Support vector machines (SVM), AdaBoostM1, Bagging), metabolism prediction models were established based on metabolism data released by Sheridan et al. Four major biotransformations, including aliphatic C-hydroxylation, aromatic C-hydroxylation, N-dealkylation and O-dealkylation, were involved. For validation, the overall accuracies of all four biotransformations exceeded 0.95. For receiver operating characteristic (ROC) analysis, each of these models gave a significant area under curve (AUC) value >0.98. In addition, an external test was performed based on dataset published previously. As a result, 87.7% of the potential SOMs were correctly identified by our four models. In summary, four MMRS-based models were established, which can be used to predict the metabolism mediated by CYP3A4, 2D6, and 2C9 with high accuracy. View Full-Text
Keywords: CYP3A4; CYP2D6; CYP2C9; microsomal metabolic reaction system; metabolism prediction; classification; feature selection CYP3A4; CYP2D6; CYP2C9; microsomal metabolic reaction system; metabolism prediction; classification; feature selection
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MDPI and ACS Style

He, S.-B.; Li, M.-M.; Zhang, B.-X.; Ye, X.-T.; Du, R.-F.; Wang, Y.; Qiao, Y.-J. Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System. Int. J. Mol. Sci. 2016, 17, 1686.

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