ArticleScience translational medicine2015
Classic reaction kinetics can explain complex patterns of antibiotic action.
Article in Science translational medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 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
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pharmacokinetic-pharmacodynamic modeling using combined time-kill data for meropenem and colistin or polymyxin B combination effects inAntimicrobial agents and chemotherapy · 2026Pooled it
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- Shielded by the dead: how killed bacteria shape the dynamics and evolution of innate immunity.bioRxiv : the preprint server for biology · 2026Article
- Efflux pumps control intracellular drug-target kinetics by limiting rebinding in bacteria.Science advances · 2026Article
- Drug delivery dynamics dictate evolution of bacterial antibiotic responses.The ISME journal · 2025Article
- Self-assembly antimicrobial peptide for treatment of multidrug-resistant bacterial infection.Journal of nanobiotechnology · 2024Article
- Dynamics of drug delivery determines course of evolution of antibiotic responses in bacteria.bioRxiv : the preprint server for biology · 2024Article
- Integrating bacterial molecular genetics with chemical biology for renewed antibacterial drug discovery.The Biochemical journal · 2024Review
- A low-footprint, fluorescence-based bacterial time-kill assay for estimating dose-dependent cell death dynamics.bioRxiv : the preprint server for biology · 2024Article
- The pharmacokinetic-pharmacodynamic modelling framework as a tool to predict drug resistance evolution.Microbiology (Reading, England) · 2023Article
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- High rifampicin peak plasma concentrations accelerate the slow phase of bacterial decline in tuberculosis patients: Evidence for heteroresistance.PLoS computational biology · 2023Article
- Survival ofFrontiers in microbiology · 2023Article
- Effects of Sub-Minimum Inhibitory Concentrations of Imipenem and Colistin on Expression of Biofilm-Specific Antibiotic Resistance and Virulence Genes inInternational journal of molecular sciences · 2022Article
- vCOMBAT: a novel tool to create and visualize a computational model of bacterial antibiotic target-binding.BMC bioinformatics · 2022Article
- Mechanisms of antibiotic action shape the fitness landscapes of resistance mutations.Computational and structural biotechnology journal · 2022Article
- Identification and Characterization of Pleiotropic High-Persistence Mutations in the Beta Subunit of the Bacterial RNA Polymerase.Antimicrobial agents and chemotherapy · 2021Article
- Mathematical Modeling of Remdesivir to Treat COVID-19: Can Dosing Be Optimized?Pharmaceutics · 2021Article
- Inoculum effect of antimicrobial peptides.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Finding optimal dosing strategies for treating bacterial infections is extremely difficult, and improving therapy requires costly and time-intensive experiments. To date, an incomplete mechanistic understanding of drug effects has limited our ability to make accurate quantitative predictions of drug-mediated bacterial killing and impeded the rational design of antibiotic treatment strategies. Three poorly understood phenomena complicate predictions of antibiotic activity: post-antibiotic growth suppression, density-dependent antibiotic effects, and persister cell formation. We show that chemical binding kinetics alone are sufficient to explain these three phenomena, using single-cell data and time-kill curves of Escherichia coli and Vibrio cholerae exposed to a variety of antibiotics in combination with a theoretical model that links chemical reaction kinetics to bacterial population biology. Our model reproduces existing observations, has a high predictive power across different experimental setups (R(2) = 0.86), and makes several testable predictions, which we verified in new experiments and by analyzing published data from a clinical trial on tuberculosis therapy. Although a variety of biological mechanisms have previously been invoked to explain post-antibiotic growth suppression, density-dependent antibiotic effects, and especially persister cell formation, our findings reveal that a simple model that considers only binding kinetics provides a parsimonious and unifying explanation for these three complex, phenotypically distinct behaviours. Current antibiotic and other chemotherapeutic regimens are often based on trial and error or expert opinion. Our "chemical reaction kinetics"-based approach may inform new strategies, which are based on rational design.
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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.