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Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9
Cytochrome P450 2C9 (CYP2C9) is a major drug-metabolizing enzyme that represents 20% of the hepatic CYPs and is responsible for the metabolism of 15% of drugs. A general concern in drug discovery is to avoid the inhibition of CYP leading to toxic drug accumulation and adverse drug–drug interactions....
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8820617/ https://www.ncbi.nlm.nih.gov/pubmed/35081108 http://dx.doi.org/10.1371/journal.pcbi.1009820 |
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author | Goldwaser, Elodie Laurent, Catherine Lagarde, Nathalie Fabrega, Sylvie Nay, Laure Villoutreix, Bruno O. Jelsch, Christian Nicot, Arnaud B. Loriot, Marie-Anne Miteva, Maria A. |
author_facet | Goldwaser, Elodie Laurent, Catherine Lagarde, Nathalie Fabrega, Sylvie Nay, Laure Villoutreix, Bruno O. Jelsch, Christian Nicot, Arnaud B. Loriot, Marie-Anne Miteva, Maria A. |
author_sort | Goldwaser, Elodie |
collection | PubMed |
description | Cytochrome P450 2C9 (CYP2C9) is a major drug-metabolizing enzyme that represents 20% of the hepatic CYPs and is responsible for the metabolism of 15% of drugs. A general concern in drug discovery is to avoid the inhibition of CYP leading to toxic drug accumulation and adverse drug–drug interactions. However, the prediction of CYP inhibition remains challenging due to its complexity. We developed an original machine learning approach for the prediction of drug-like molecules inhibiting CYP2C9. We created new predictive models by integrating CYP2C9 protein structure and dynamics knowledge, an original selection of physicochemical properties of CYP2C9 inhibitors, and machine learning modeling. We tested the machine learning models on publicly available data and demonstrated that our models successfully predicted CYP2C9 inhibitors with an accuracy, sensitivity and specificity of approximately 80%. We experimentally validated the developed approach and provided the first identification of the drugs vatalanib, piriqualone, ticagrelor and cloperidone as strong inhibitors of CYP2C9 with IC values <18 μM and sertindole, asapiprant, duvelisib and dasatinib as moderate inhibitors with IC50 values between 40 and 85 μM. Vatalanib was identified as the strongest inhibitor with an IC50 value of 0.067 μM. Metabolism assays allowed the characterization of specific metabolites of abemaciclib, cloperidone, vatalanib and tarafenacin produced by CYP2C9. The obtained results demonstrate that such a strategy could improve the prediction of drug-drug interactions in clinical practice and could be utilized to prioritize drug candidates in drug discovery pipelines. |
format | Online Article Text |
id | pubmed-8820617 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-88206172022-02-08 Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9 Goldwaser, Elodie Laurent, Catherine Lagarde, Nathalie Fabrega, Sylvie Nay, Laure Villoutreix, Bruno O. Jelsch, Christian Nicot, Arnaud B. Loriot, Marie-Anne Miteva, Maria A. PLoS Comput Biol Research Article Cytochrome P450 2C9 (CYP2C9) is a major drug-metabolizing enzyme that represents 20% of the hepatic CYPs and is responsible for the metabolism of 15% of drugs. A general concern in drug discovery is to avoid the inhibition of CYP leading to toxic drug accumulation and adverse drug–drug interactions. However, the prediction of CYP inhibition remains challenging due to its complexity. We developed an original machine learning approach for the prediction of drug-like molecules inhibiting CYP2C9. We created new predictive models by integrating CYP2C9 protein structure and dynamics knowledge, an original selection of physicochemical properties of CYP2C9 inhibitors, and machine learning modeling. We tested the machine learning models on publicly available data and demonstrated that our models successfully predicted CYP2C9 inhibitors with an accuracy, sensitivity and specificity of approximately 80%. We experimentally validated the developed approach and provided the first identification of the drugs vatalanib, piriqualone, ticagrelor and cloperidone as strong inhibitors of CYP2C9 with IC values <18 μM and sertindole, asapiprant, duvelisib and dasatinib as moderate inhibitors with IC50 values between 40 and 85 μM. Vatalanib was identified as the strongest inhibitor with an IC50 value of 0.067 μM. Metabolism assays allowed the characterization of specific metabolites of abemaciclib, cloperidone, vatalanib and tarafenacin produced by CYP2C9. The obtained results demonstrate that such a strategy could improve the prediction of drug-drug interactions in clinical practice and could be utilized to prioritize drug candidates in drug discovery pipelines. Public Library of Science 2022-01-26 /pmc/articles/PMC8820617/ /pubmed/35081108 http://dx.doi.org/10.1371/journal.pcbi.1009820 Text en © 2022 Goldwaser et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Goldwaser, Elodie Laurent, Catherine Lagarde, Nathalie Fabrega, Sylvie Nay, Laure Villoutreix, Bruno O. Jelsch, Christian Nicot, Arnaud B. Loriot, Marie-Anne Miteva, Maria A. Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9 |
title | Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9 |
title_full | Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9 |
title_fullStr | Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9 |
title_full_unstemmed | Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9 |
title_short | Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9 |
title_sort | machine learning-driven identification of drugs inhibiting cytochrome p450 2c9 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8820617/ https://www.ncbi.nlm.nih.gov/pubmed/35081108 http://dx.doi.org/10.1371/journal.pcbi.1009820 |
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