McCoubrey, LE;
Elbadawi, M;
Orlu, M;
Gaisford, S;
Basit, AW;
(2021)
Machine learning uncovers adverse drug effects on intestinal bacteria.
Pharmaceutics
, 13
(7)
, Article 1026. 10.3390/pharmaceutics13071026.
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Abstract
The human gut microbiome, composed of trillions of microorganisms, plays an essential role in human health. Many factors shape gut microbiome composition over the life span, including changes to diet, lifestyle, and medication use. Though not routinely tested during drug development, drugs can exert profound effects on the gut microbiome, potentially altering its functions and promoting disease. This study develops a machine learning (ML) model to predict whether drugs will impair the growth of 40 gut bacterial strains. Trained on over 18,600 drug–bacteria interactions, 13 distinct ML models are built and compared, including tree-based, ensemble, and artificial neural network techniques. Following hyperparameter tuning and multi-metric evaluation, a lead ML model is selected: a tuned extra trees algorithm with performances of AUROC: 0.857 (±0.014), recall: 0.587 (±0.063), precision: 0.800 (±0.053), and f1: 0.666 (±0.042). This model can be used by the pharmaceutical industry during drug development and could even be adapted for use in clinical settings.
| Type: | Article |
|---|---|
| Title: | Machine learning uncovers adverse drug effects on intestinal bacteria |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/pharmaceutics13071026 |
| Publisher version: | https://doi.org/10.3390/pharmaceutics13071026 |
| Language: | English |
| Additional information: | This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited |
| Keywords: | artificial intelligence; microbiota; drug discovery and development; metabolism of biopharmaceuticals and medicines; in silico; computational prediction and screening; toxicology; digital health; xenobiotics |
| 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/10131145 |
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