ArticleCell reports2020
Machine Learning Uncovers Food- and Excipient-Drug Interactions.
Article in Cell reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
23 citing papers in PubMed.
- Active learning in pharmaceutical 3D printing: a multi-dataset comparison.Drug delivery and translational research · 2026Article
- Machine-Learning-Driven Optimization of Functional Excipients and Their Biointeractions in Drug Formulations.ACS pharmacology & translational science · 2026Review
- Effects of vitamins A and E on eprinomectin transport in Caco-2 cells.Veterinarni medicina · 2026Article
- Breaking the antimicrobial resistance dilemma: marine natural products, a treasure trove for the development of new anti-Marine life science & technology · 2026Review
- Identifying Antibiotic Effects of Investigational Drugs on Commensal Bacteria with Machine Learning.ACS pharmacology & translational science · 2026Article
- Unveiling the bioactive landscape of drug inactive ingredients (DIGs) using deep transfer learning.Acta pharmaceutica Sinica. B · 2026Article
- Artificial Intelligence in Pharmaceutical Drug Development: Transforming Formulation and Innovation.Current drug discovery technologies · 2026Review
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
- Artificial Intelligence in Natural Product Drug Discovery: Current Applications and Future Perspectives.Journal of medicinal chemistry · 2025Review
- Artificial Intelligence and Machine Learning Applications to Pharmacokinetic Modeling and Dose Prediction of Antibiotics: A Scoping Review.Antibiotics (Basel, Switzerland) · 2024Article
- Understanding of Wetting Mechanism Toward the Sticky Powder and Machine Learning in Predicting Granule Size Distribution Under High Shear Wet Granulation.AAPS PharmSciTech · 2024Article
- In Vivo Regulation of Small Molecule Natural Products, Antioxidants, and Nutrients by OAT1 and OAT3.Nutrients · 2024Article
- DDID: a comprehensive resource for visualization and analysis of diet-drug interactions.Briefings in bioinformatics · 2024Article
- Artificial intelligence for natural product drug discovery.Nature reviews. Drug discovery · 2023Review
- DrugMAP: molecular atlas and pharma-information of all drugs.Nucleic acids research · 2023Article
- Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy.World journal of gastroenterology · 2022Article
- Progress in the Application of Food-Grade Emulsions.Foods (Basel, Switzerland) · 2022Review
- A novel graph mining approach to predict and evaluate food-drug interactions.Scientific reports · 2022Article
- Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota.Pharmaceutics · 2021Article
- A Non-Nutritive Feeding Intervention Alters the Expression of Efflux Transporters in the Gastrointestinal Tract.Pharmaceutics · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Inactive ingredients and generally recognized as safe compounds are regarded by the US Food and Drug Administration (FDA) as benign for human consumption within specified dose ranges, but a growing body of research has revealed that many inactive ingredients might have unknown biological effects at these concentrations and might alter treatment outcomes. To speed up such discoveries, we apply state-of-the-art machine learning to delineate currently unknown biological effects of inactive ingredients-focusing on P-glycoprotein (P-gp) and uridine diphosphate-glucuronosyltransferase-2B7 (UGT2B7), two proteins that impact the pharmacokinetics of approximately 20% of FDA-approved drugs. Our platform identifies vitamin A palmitate and abietic acid as inhibitors of P-gp and UGT2B7, respectively; in silico, in vitro, ex vivo, and in vivo validations support these interactions. Our predictive framework can elucidate biological effects of commonly consumed chemical matter with implications on food- and excipient-drug interactions and functional drug formulation development.
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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.