ArticleProceedings of the National Academy of Sciences of the United States of America2016
Prediction of multidimensional drug dose responses based on measurements of drug pairs.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers.
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Who cites it
98 citing papers in PubMed.
- Prospectively predicting BPaMZ phase IIb/III trial outcomes using a translational mouse-to-human platform.Antimicrobial agents and chemotherapy · 2024Trial
- From molecular pathogenesis to novel Therapeutic approaches - a review of recent advances in the treatment of peripheral T cell Lymphomas.Biomarker research · 2026Review
- CDCR-Rank: a computational model for predicting drug combination dose response using ranking-based optimization.Bioinformatics advances · 2026Article
- Prediction of mixture toxicity of essential oil constituents using nonparametric and parametric models against Musca domestica L.Pest management science · 2025Article
- Tailoring combinational therapy with Monte Carlo method-based regression modeling.Fundamental research · 2025Article
- Preventing Preterm Birth: The Search for Tocolytic Synergism.Reproductive sciences (Thousand Oaks, Calif.) · 2025Review
- Challenges to determine synergistic drug interactions in mice.Nature communications · 2025Article
- PanThera: predictive analysis of higher-order combination therapies using deep neural networks.Briefings in bioinformatics · 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
- Resolving discrepancies between chimeric and multiplicative measures of higher-order epistasis.Nature communications · 2025Article
- Dynamic collateral sensitivity profiles highlight opportunities and challenges for optimizing antibiotic treatments.PLoS biology · 2025Article
- Optimizing drug synergy prediction through categorical embeddings in deep neural networks.Biology methods & protocols · 2025Article
- Drug combinations targeting antibiotic resistance.npj antimicrobials and resistance · 2024Review
- Structure and inference in hypergraphs with node attributes.Nature communications · 2024Article
- Higher-order correlations reveal complex memory in temporal hypergraphs.Nature communications · 2024Article
- Data-driven learning of structure augments quantitative prediction of biological responses.PLoS computational biology · 2024Article
- Use of multiple pharmacodynamic measures to deconstruct the Nix-TB regimen in a short-course murine model of tuberculosis.Antimicrobial agents and chemotherapy · 2024Article
- A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment.Cell systems · 2024Article
- The evolution of resistance to synergistic multi-drug combinations is more complex than evolving resistance to each individual drug component.Evolutionary applications · 2023Article
38 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Finding potent multidrug combinations against cancer and infections is a pressing therapeutic challenge; however, screening all combinations is difficult because the number of experiments grows exponentially with the number of drugs and doses. To address this, we present a mathematical model that predicts the effects of three or more antibiotics or anticancer drugs at all doses based only on measurements of drug pairs at a few doses, without need for mechanistic information. The model provides accurate predictions on available data for antibiotic combinations, and on experiments presented here on the response matrix of three cancer drugs at eight doses per drug. This approach offers a way to search for effective multidrug combinations using a small number of experiments.
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