ArticlePLoS computational biology2018
Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed, 47 citations in OpenAlex.
- TACTIC: A transfer learning framework to predict drug interactions in emerging pathogens.Cell reports methods · 2026Article
- Transcriptome-constrained genome-scale metabolic modeling reveals central carbon and amino acid metabolic reprogramming underlying colistin-sulbactam synergy inAntimicrobial agents and chemotherapy · 2026Article
- A comprehensive review of cluster methods for drug-drug interaction network.Quantitative biology (Beijing, China) · 2026Review
- A Metabolism-Informed Neural Network Identifies Pathways Influencing the Potency and Toxicity of Antimicrobial Combinations.npj drug discovery · 2026Article
- Human organoids and organ-on-chip systems for AI-guided therapeutic discovery against emerging and re-emerging infections.Frontiers in pharmacology · 2026Review
- Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.Pharmaceutics · 2025Review
- Large-scale combination screens reveal small-molecule sensitization of antibiotic-resistant gram-negative ESKAPE pathogens.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- GEODE: anFrontiers in pharmacology · 2025Article
- Transfer learning predicts species-specific drug interactions in emerging pathogens.bioRxiv : the preprint server for biology · 2024Article
- Article
- A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions.PNAS nexus · 2022Article
- Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design.PLoS computational biology · 2022Article
- Discovery of Natural Products With Antifungal Potential Through Combinatorial Synergy.Frontiers in microbiology · 2022Article
- ACDB: An Antibiotic Combination DataBase.Frontiers in pharmacology · 2022Article
- Prediction of Synergistic Antibiotic Combinations by Graph Learning.Frontiers in pharmacology · 2022Article
- Systematic measurement of combination-drug landscapes to predict in vivo treatment outcomes for tuberculosis.Cell systems · 2021Article
- Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility.mSphere · 2021Article
- The future of antibiotics begins with discovering new combinations.Annals of the New York Academy of Sciences · 2021Review
- Systematic Prediction of Antifungal Drug Synergy by Chemogenomic Screening inFrontiers in fungal biology · 2021Article
- Crippling life support for SARS-CoV-2 and other viruses through synthetic lethality.The Journal of cell biology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Antibiotics need to be effective in diverse environments in vivo. However, the pathogen microenvironment can have a significant impact on antibiotic potency. Further, antibiotics are increasingly used in combinations to combat resistance, yet, the effect of microenvironments on drug-combination efficacy is unknown. To exhaustively explore the impact of diverse microenvironments on drug-combinations, here we develop a computational framework-Metabolism And GENomics-based Tailoring of Antibiotic regimens (MAGENTA). MAGENTA uses chemogenomic profiles of individual drugs and metabolic perturbations to predict synergistic or antagonistic drug-interactions in different microenvironments. We uncovered antibiotic combinations with robust synergy across nine distinct environments against both E. coli and A. baumannii by searching through 2556 drug-combinations of 72 drugs. MAGENTA also accurately predicted the change in efficacy of bacteriostatic and bactericidal drug-combinations during growth in glycerol media, which we confirmed experimentally in both microbes. Our approach identified genes in glycolysis and glyoxylate pathway as top predictors of synergy and antagonism respectively. Our systems approach enables tailoring of antibiotic therapies based on the pathogen microenvironment.
Indexed as
Identifiers
What OpenQuestion holds
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.