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Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System
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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5085718/ https://www.ncbi.nlm.nih.gov/pubmed/27735849 http://dx.doi.org/10.3390/ijms17101686 |
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author | He, Shuai-Bing Li, Man-Man Zhang, Bai-Xia Ye, Xiao-Tong Du, Ran-Feng Wang, Yun Qiao, Yan-Jiang |
author_facet | He, Shuai-Bing Li, Man-Man Zhang, Bai-Xia Ye, Xiao-Tong Du, Ran-Feng Wang, Yun Qiao, Yan-Jiang |
author_sort | He, Shuai-Bing |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5085718 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-50857182016-11-01 Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System He, Shuai-Bing Li, Man-Man Zhang, Bai-Xia Ye, Xiao-Tong Du, Ran-Feng Wang, Yun Qiao, Yan-Jiang Int J Mol Sci Article 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. MDPI 2016-10-09 /pmc/articles/PMC5085718/ /pubmed/27735849 http://dx.doi.org/10.3390/ijms17101686 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article He, Shuai-Bing Li, Man-Man Zhang, Bai-Xia Ye, Xiao-Tong Du, Ran-Feng Wang, Yun Qiao, Yan-Jiang Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System |
title | Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System |
title_full | Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System |
title_fullStr | Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System |
title_full_unstemmed | Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System |
title_short | Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System |
title_sort | construction of metabolism prediction models for cyp450 3a4, 2d6, and 2c9 based on microsomal metabolic reaction system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5085718/ https://www.ncbi.nlm.nih.gov/pubmed/27735849 http://dx.doi.org/10.3390/ijms17101686 |
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