ArticleProceedings of the National Academy of Sciences of the United States of America2024
Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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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
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational paradigms for antimicrobial resistance prediction: integrating multi-omics, structural modeling, and foundation artificial intelligence systems.Briefings in bioinformatics · 2026Pooled it
- A microbial therapy-mimicry assay shows how spatial resource dynamics control resistance escape.Nature ecology & evolution · 2026Article
- Review
- Collateral sensitivity-harnessing microbial vulnerabilities as a solution to antimicrobial resistance.Molecular biology reports · 2026Review
- Artificial Intelligence Across the Drug Development Lifecycle.Medical sciences (Basel, Switzerland) · 2026Review
- Smart Optogenetics for Real-Time Automated Control of Cardiac Electrical Activity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Stochastic Evolutionary Control in Heterogeneous Populations.bioRxiv : the preprint server for biology · 2026Article
- Fabrication, Characterization, and Antibacterial Performance of Polyvinyl Alcohol (PVA)-Zinc Oxide (ZnO) Thin Films for Packaging Applications.Applied biochemistry and biotechnology · 2026Article
- Optimizing genomic sampling for demographic and epidemiological inference with Markov decision processes.Genetics · 2026Article
- Uncloaking the black-box: the need for explainable artificial intelligence in clinical microbiology and infectious diseases applications.Frontiers in public health · 2026Review
- Mathematical Oncology: How Modeling Is Transforming Clinical Decision-Making.Cancer research · 2025Review
- Reinforcement learning for adaptive control of phenotypically heterogeneous bacterial populations.bioRxiv : the preprint server for biology · 2025Article
- Computational framework for streamlining the success of sequential antibiotic therapy.npj antimicrobials and resistance · 2025Article
- Evolution under vancomycin selection drives divergent collateral sensitivity patterns inProceedings of the National Academy of Sciences of the United States of America · 2025Article
- Personalizing chemotherapy drug selection using a novel transcriptomic chemogram.PLoS computational biology · 2025Article
- Optimal control of multiple myeloma assuming drug resistance and off-target effects.PLoS computational biology · 2025Article
- Fitness seascapes are necessary for realistic modeling of the evolutionary response to drug therapy.Science advances · 2025Article
- The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance.Computational and structural biotechnology journal · 2025Review
- Linking spatial drug heterogeneity to microbial growth dynamics in theory and experiment.bioRxiv : the preprint server for biology · 2024Article
- From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review.Journal of medical systems · 2024Review
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Authors and funding
4 authors.
Funding
Abstract
Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent, or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known a priori. In this study, we extend this work using a reinforcement learning (RL) approach capable of learning effective drug cycling policies in a system defined by empirically measured fitness landscapes. Crucially, we show that it is possible to learn effective drug cycling policies despite the problems of noisy, limited, or delayed measurement. Given access to a panel of 15 [Formula: see text]-lactam antibiotics with which to treat the simulated
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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.