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

International Journal of Molecular Sciences

Bai-Xia Zhang¹  Yan-Jiang Qiao²  Yun Wang²  Ran-Feng Du²  Man-Man Li²  Shuai-Bing He²  Xiao-Tong Ye² 

[1] College of Chinese Medicine, Hebei University, Baoding 071002, China;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;

关键词: CYP3A4;    CYP2D6;    CYP2C9;    microsomal metabolic reaction system;    metabolism prediction;    classification;    feature selection;   

DOI  :  10.3390/ijms17101686

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown