ArticlePharmaceutics2021
Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota.
Article in Pharmaceutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- High-throughput identification of bacterial β-glucuronidase inhibitors using machine learning.Gut microbes · 2026Article
- Machine learning based approaches for structure activity relationship analysis of heparanase inhibitors.Scientific reports · 2026Article
- Review
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
- Integrating the gut microbiome and pharmacology.Science translational medicine · 2024Review
- A study of machine learning to predict NRDS severity based on lung ultrasound score and clinical indicators.Frontiers in medicine · 2024Article
- Machine learning model of imipenem-resistantHealth science reports · 2023Article
- The Interplay between Gut Microbiota and Oral Medications and Its Impact on Advancing Precision Medicine.Metabolites · 2023Review
- Application of artificial intelligence approaches to predict the metabolism of xenobiotic molecules by human gut microbiome.Frontiers in microbiology · 2023Review
- Ileocolonic-Targeted JAK Inhibitor: A Safer and More Effective Treatment for Inflammatory Bowel Disease.Pharmaceutics · 2022Article
- Gut Microbiome: The Interplay of an "Invisible Organ" with Herbal Medicine and Its Derived Compounds in Chronic Metabolic Disorders.International journal of environmental research and public health · 2022Review
- Microbiome as an immune regulator in health, disease, and therapeutics.Advanced drug delivery reviews · 2022Review
- Machine Learning in Drug Metabolism Study.Current drug metabolism · 2022Article
- Microbial metabolism marvels: a comprehensive review of microbial drug transformation capabilities.Gut microbesReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.