ArticleScientific reports2019
Design of high-order antibiotic combinations against M. tuberculosis by ranking and exclusion.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Bioactivity-driven prediction of antibacterial synergy using machine learning models.PLoS computational biology · 2026Article
- TACTIC: A transfer learning framework to predict drug interactions in emerging pathogens.Cell reports methods · 2026Article
- Assessing the efficacy of combined treatment using bedaquiline against clinical isolates of multidrug-resistant Mycobacterium tuberculosis in vitro and the macrophage model.BMC microbiology · 2026Article
- Isoniazid potentiates tigecycline to kill methicillin-resistantEmerging microbes & infections · 2025Article
- A Machine Learning-Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia.Cancer research · 2025Article
- Interactions within higher-order antibiotic combinations do not influence the rate of adaptation in bacteria.Evolution; international journal of organic evolution · 2025Article
- Flash optimization of drug combinations for Acinetobacter baumannii with IDentif.AI-AMR.npj antimicrobials and resistance · 2025Article
- Assessment of tuberculosis drug efficacy using preclinical animal models and in vitro predictive techniques.npj antimicrobials and resistance · 2024Article
- Identification of gene targets that potentiate the action of rifampicin onMicrobiology (Reading, England) · 2024Article
- Transfer learning predicts species-specific drug interactions in emerging pathogens.bioRxiv : the preprint server for biology · 2024Article
- A host-directed oxadiazole compound potentiates antituberculosis treatment via zinc poisoning in human macrophages and in a mouse model of infection.PLoS biology · 2024Article
- Spermine enhances the activity of anti-tuberculosis drugs.Microbiology spectrum · 2024Article
- Developing synergistic drug combinations to restore antibiotic sensitivity in drug-resistantAntimicrobial agents and chemotherapy · 2023Article
- Article
- Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design.PLoS computational biology · 2022Article
- Pre-Clinical Tools for Predicting Drug Efficacy in Treatment of Tuberculosis.Microorganisms · 2022Review
- Article
- Identification of Drug Combination Therapies for SARS-CoV-2: A Molecular Dynamics Simulations Approach.Drug design, development and therapy · 2022Article
- Impact of Simulated Intestinal Fluids on Dissolution, Solution Chemistry, and Membrane Transport of Amorphous Multidrug Formulations.Molecular pharmaceutics · 2021Article
- Deep learning identifies synergistic drug combinations for treating COVID-19.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
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Authors and funding
2 authors.
Funding
Abstract
Combinations of more than two drugs are routinely used for the treatment of pathogens and tumors. High-order combinations may be chosen due to their non-overlapping resistance mechanisms or for favorable drug interactions. Synergistic/antagonistic interactions occur when the combination has a higher/lower effect than the sum of individual drug effects. The standard treatment of Mycobacterium tuberculosis (Mtb) is an additive cocktail of three drugs which have different targets. Herein, we experimentally measured all 190 pairwise interactions among 20 antibiotics against Mtb growth. We used the pairwise interaction data to rank all possible high-order combinations by strength of synergy/antagonism. We used drug interaction profile correlation as a proxy for drug similarity to establish exclusion criteria for ideal combination therapies. Using this ranking and exclusion design (R/ED) framework, we modeled ways to improve the standard 3-drug combination with the addition of new drugs. We applied this framework to find the best 4-drug combinations against drug-resistant Mtb by adding new exclusion criteria to R/ED. Finally, we modeled alternating 2-order combinations as a cycling treatment and found optimized regimens significantly reduced the overall effective dose. R/ED provides an adaptable framework for the design of high-order drug combinations against any pathogen or tumor.
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