ArticlePLoS computational biology2019
Predicting gastrointestinal drug effects using contextualized metabolic models.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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.
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Who cites it
12 citing papers in PubMed.
- Machine Learning-Integrated Metabolomics for Precision Pharmacotherapy: Advances, Challenges, and Clinical Translation.Metabolites · 2026Review
- Model-based inference of enzyme inhibitions from perturbation-induced metabolic dynamics.bioRxiv : the preprint server for biology · 2026Article
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design.Synthetic and systems biotechnology · 2024Article
- Direct-acting antiviral agent use and gastrointestinal safety in patients with chronic hepatitis C: a pharmacovigilance study based on FDA Adverse Event Reporting System.International journal of clinical pharmacy · 2023Article
- Whole-genome sequencing and genome-scale metabolic modeling of Chromohalobacter canadensis 85B to explore its salt tolerance and biotechnological use.MicrobiologyOpen · 2022Article
- Connecting omics signatures and revealing biological mechanisms with iLINCS.Nature communications · 2022Article
- Exploring synergies between plant metabolic modelling and machine learning.Computational and structural biotechnology journal · 2022Review
- Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacteriumSTAR protocols · 2021Article
- Path to improving the life cycle and quality of genome-scale models of metabolism.Cell systems · 2021Review
- Digital microbiology.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2020Review
- A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Gastrointestinal side effects are among the most common classes of adverse reactions associated with orally absorbed drugs. These effects decrease patient compliance with the treatment and induce undesirable physiological effects. The prediction of drug action on the gut wall based on in vitro data solely can improve the safety of marketed drugs and first-in-human trials of new chemical entities. We used publicly available data of drug-induced gene expression changes to build drug-specific small intestine epithelial cell metabolic models. The combination of measured in vitro gene expression and in silico predicted metabolic rates in the gut wall was used as features for a multilabel support vector machine to predict the occurrence of side effects. We showed that combining local gut wall-specific metabolism with gene expression performs better than gene expression alone, which indicates the role of small intestine metabolism in the development of adverse reactions. Furthermore, we reclassified FDA-labeled drugs with respect to their genetic and metabolic profiles to show hidden similarities between seemingly different drugs. The linkage of xenobiotics to their transcriptomic and metabolic profiles could take pharmacology far beyond the usual indication-based classifications.
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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.