Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota

Pharmaceutics

Stavriani Thomaidou¹  Laura E. McCoubrey¹  Moe Elbadawi¹  Abdul W. Basit¹  Simon Gaisford¹  Mine Orlu¹ 

[1] Department of Pharmaceutics, UCL School of Pharmacy, University College London, London WC1N 1AX, UK;

关键词: artificial intelligence;    classification;    semi-supervised learning;    gastrointestinal microbiome;    drug stability;    drug discovery and development;   

DOI  :  10.3390/pharmaceutics13122001

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown