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Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule

BACKGROUND: Different isoforms of Cytochrome P450 (CYP) metabolized different types of substrates (or drugs molecule) and make them soluble during biotransformation. Therefore, fate of any drug molecule depends on how they are treated or metabolized by CYP isoform. There is a need to develop models...

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Autores principales: Mishra, Nitish K, Agarwal, Sandhya, Raghava, Gajendra PS
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2912882/
https://www.ncbi.nlm.nih.gov/pubmed/20637097
http://dx.doi.org/10.1186/1471-2210-10-8
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author Mishra, Nitish K
Agarwal, Sandhya
Raghava, Gajendra PS
author_facet Mishra, Nitish K
Agarwal, Sandhya
Raghava, Gajendra PS
author_sort Mishra, Nitish K
collection PubMed
description BACKGROUND: Different isoforms of Cytochrome P450 (CYP) metabolized different types of substrates (or drugs molecule) and make them soluble during biotransformation. Therefore, fate of any drug molecule depends on how they are treated or metabolized by CYP isoform. There is a need to develop models for predicting substrate specificity of major isoforms of P450, in order to understand whether a given drug will be metabolized or not. This paper describes an in-silico method for predicting the metabolizing capability of major isoforms (e.g. CYP 3A4, 2D6, 1A2, 2C9 and 2C19). RESULTS: All models were trained and tested on 226 approved drug molecules. Firstly, 2392 molecular descriptors for each drug molecule were calculated using various softwares. Secondly, best 41 descriptors were selected using general and genetic algorithm. Thirdly, Support Vector Machine (SVM) based QSAR models were developed using 41 best descriptors and achieved an average accuracy of 86.02%, evaluated using fivefold cross-validation. We have also evaluated the performance of our model on an independent dataset of 146 drug molecules and achieved average accuracy 70.55%. In addition, SVM based models were developed using 26 Chemistry Development Kit (CDK) molecular descriptors and achieved an average accuracy of 86.60%. CONCLUSIONS: This study demonstrates that SVM based QSAR model can predict substrate specificity of major CYP isoforms with high accuracy. These models can be used to predict isoform responsible for metabolizing a drug molecule. Thus these models can used to understand whether a molecule will be metabolized or not. This is possible to develop highly accurate models for predicting substrate specificity of major isoforms using CDK descriptors. A web server MetaPred has been developed for predicting metabolizing isoform of a drug molecule http://crdd.osdd.net/raghava/metapred/.
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spelling pubmed-29128822010-08-02 Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule Mishra, Nitish K Agarwal, Sandhya Raghava, Gajendra PS BMC Pharmacol Research Article BACKGROUND: Different isoforms of Cytochrome P450 (CYP) metabolized different types of substrates (or drugs molecule) and make them soluble during biotransformation. Therefore, fate of any drug molecule depends on how they are treated or metabolized by CYP isoform. There is a need to develop models for predicting substrate specificity of major isoforms of P450, in order to understand whether a given drug will be metabolized or not. This paper describes an in-silico method for predicting the metabolizing capability of major isoforms (e.g. CYP 3A4, 2D6, 1A2, 2C9 and 2C19). RESULTS: All models were trained and tested on 226 approved drug molecules. Firstly, 2392 molecular descriptors for each drug molecule were calculated using various softwares. Secondly, best 41 descriptors were selected using general and genetic algorithm. Thirdly, Support Vector Machine (SVM) based QSAR models were developed using 41 best descriptors and achieved an average accuracy of 86.02%, evaluated using fivefold cross-validation. We have also evaluated the performance of our model on an independent dataset of 146 drug molecules and achieved average accuracy 70.55%. In addition, SVM based models were developed using 26 Chemistry Development Kit (CDK) molecular descriptors and achieved an average accuracy of 86.60%. CONCLUSIONS: This study demonstrates that SVM based QSAR model can predict substrate specificity of major CYP isoforms with high accuracy. These models can be used to predict isoform responsible for metabolizing a drug molecule. Thus these models can used to understand whether a molecule will be metabolized or not. This is possible to develop highly accurate models for predicting substrate specificity of major isoforms using CDK descriptors. A web server MetaPred has been developed for predicting metabolizing isoform of a drug molecule http://crdd.osdd.net/raghava/metapred/. BioMed Central 2010-07-16 /pmc/articles/PMC2912882/ /pubmed/20637097 http://dx.doi.org/10.1186/1471-2210-10-8 Text en Copyright ©2010 Mishra et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Mishra, Nitish K
Agarwal, Sandhya
Raghava, Gajendra PS
Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule
title Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule
title_full Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule
title_fullStr Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule
title_full_unstemmed Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule
title_short Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule
title_sort prediction of cytochrome p450 isoform responsible for metabolizing a drug molecule
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2912882/
https://www.ncbi.nlm.nih.gov/pubmed/20637097
http://dx.doi.org/10.1186/1471-2210-10-8
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