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Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota
Over 150 drugs are currently recognised as being susceptible to metabolism or bioaccumulation (together described as depletion) by gastrointestinal microorganisms; however, the true number is likely higher. Microbial drug depletion is often variable between and within individuals, depending on their...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8707855/ https://www.ncbi.nlm.nih.gov/pubmed/34959282 http://dx.doi.org/10.3390/pharmaceutics13122001 |
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author | McCoubrey, Laura E. Thomaidou, Stavriani Elbadawi, Moe Gaisford, Simon Orlu, Mine Basit, Abdul W. |
author_facet | McCoubrey, Laura E. Thomaidou, Stavriani Elbadawi, Moe Gaisford, Simon Orlu, Mine Basit, Abdul W. |
author_sort | McCoubrey, Laura E. |
collection | PubMed |
description | Over 150 drugs are currently recognised as being susceptible to metabolism or bioaccumulation (together described as depletion) by gastrointestinal microorganisms; however, the true number is likely higher. Microbial drug depletion is often variable between and within individuals, depending on their unique composition of gut microbiota. Such variability can lead to significant differences in pharmacokinetics, which may be associated with dosing difficulties and lack of medication response. In this study, literature mining and unsupervised learning were used to curate a dataset of 455 drug–microbiota interactions. From this, 11 supervised learning models were developed that could predict drugs’ susceptibility to depletion by gut microbiota. The best model, a tuned extremely randomised trees classifier, achieved performance metrics of AUROC: 75.1% ± 6.8; weighted recall: 79.2% ± 3.9; balanced accuracy: 69.0% ± 4.6; and weighted precision: 80.2% ± 3.7 when validated on 91 drugs. This machine learning model is the first of its kind and provides a rapid, reliable, and resource-friendly tool for researchers and industry professionals to screen drugs for susceptibility to depletion by gut microbiota. The recognition of drug–microbiome interactions can support successful drug development and promote better formulations and dosage regimens for patients. |
format | Online Article Text |
id | pubmed-8707855 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87078552021-12-25 Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota McCoubrey, Laura E. Thomaidou, Stavriani Elbadawi, Moe Gaisford, Simon Orlu, Mine Basit, Abdul W. Pharmaceutics Article Over 150 drugs are currently recognised as being susceptible to metabolism or bioaccumulation (together described as depletion) by gastrointestinal microorganisms; however, the true number is likely higher. Microbial drug depletion is often variable between and within individuals, depending on their unique composition of gut microbiota. Such variability can lead to significant differences in pharmacokinetics, which may be associated with dosing difficulties and lack of medication response. In this study, literature mining and unsupervised learning were used to curate a dataset of 455 drug–microbiota interactions. From this, 11 supervised learning models were developed that could predict drugs’ susceptibility to depletion by gut microbiota. The best model, a tuned extremely randomised trees classifier, achieved performance metrics of AUROC: 75.1% ± 6.8; weighted recall: 79.2% ± 3.9; balanced accuracy: 69.0% ± 4.6; and weighted precision: 80.2% ± 3.7 when validated on 91 drugs. This machine learning model is the first of its kind and provides a rapid, reliable, and resource-friendly tool for researchers and industry professionals to screen drugs for susceptibility to depletion by gut microbiota. The recognition of drug–microbiome interactions can support successful drug development and promote better formulations and dosage regimens for patients. MDPI 2021-11-25 /pmc/articles/PMC8707855/ /pubmed/34959282 http://dx.doi.org/10.3390/pharmaceutics13122001 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article McCoubrey, Laura E. Thomaidou, Stavriani Elbadawi, Moe Gaisford, Simon Orlu, Mine Basit, Abdul W. Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota |
title | Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota |
title_full | Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota |
title_fullStr | Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota |
title_full_unstemmed | Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota |
title_short | Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota |
title_sort | machine learning predicts drug metabolism and bioaccumulation by intestinal microbiota |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8707855/ https://www.ncbi.nlm.nih.gov/pubmed/34959282 http://dx.doi.org/10.3390/pharmaceutics13122001 |
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