McCoubrey, LE;
Thomaidou, S;
Elbadawi, M;
Gaisford, S;
Orlu, M;
Basit, AW;
(2021)
Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota.
Pharmaceutics
, 13
(12)
, Article 2001. 10.3390/pharmaceutics13122001.
Preview |
Text
pharmaceutics-13-02001.pdf - Published Version Download (1MB) | Preview |
Abstract
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.
Type: | Article |
---|---|
Title: | Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.3390/pharmaceutics13122001 |
Publisher version: | https://doi.org/10.3390/pharmaceutics13122001 |
Language: | English |
Additional information: | This is an open access article distributed under 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 work is properly cited. |
Keywords: | artificial intelligence; classification; semi-supervised learning; gastrointestinal microbiome; drug stability; drug discovery and development; pharmacokinetics; in silico prediction; principal component analysis; feature selection |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy > Pharmaceutics |
URI: | https://discovery.ucl.ac.uk/id/eprint/10139580 |
Archive Staff Only
View Item |