ArticleComputational and structural biotechnology journal2022
Mechanisms of antibiotic action shape the fitness landscapes of resistance mutations.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 19 citations in OpenAlex.
- Shielded by the dead: how killed bacteria shape the dynamics and evolution of innate immunity.bioRxiv : the preprint server for biology · 2026Article
- Bacterial secondary metabolites as resistance-modifying adjuvants: microbial origins, molecular mechanisms, and translational relevance.Frontiers in microbiology · 2026Review
- A General Analytic Approach to Predicting the Best Antibiotic Dosing Regimen.bioRxiv : the preprint server for biology · 2025Article
- Mitigating antimicrobial resistance by innovative solutions in AI (MARISA): a modified James Lind Alliance analysis.npj antimicrobials and resistance · 2025Article
- A low-footprint, fluorescence-based bacterial time-kill assay for estimating dose-dependent cell death dynamics.bioRxiv : the preprint server for biology · 2024Article
- Synthesis and Antibacterial Evaluation of Novel Small-Molecule Antibacterials of a Reduced Acridine Structure inMedicinal chemistry (Shariqah (United Arab Emirates)) · 2024Article
- Strategies to Overcome Antimicrobial Resistance in Nosocomial Infections, A Review and Update.Infectious disorders drug targets · 2024Review
- The pharmacokinetic-pharmacodynamic modelling framework as a tool to predict drug resistance evolution.Microbiology (Reading, England) · 2023Article
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Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
Abstract
Antibiotic-resistant pathogens are a major public health threat. A deeper understanding of how an antibiotic's mechanism of action influences the emergence of resistance would aid in the design of new drugs and help to preserve the effectiveness of existing ones. To this end, we developed a model that links bacterial population dynamics with antibiotic-target binding kinetics. Our approach allows us to derive mechanistic insights on drug activity from population-scale experimental data and to quantify the interplay between drug mechanism and resistance selection. We find that both bacteriostatic and bactericidal agents can be equally effective at suppressing the selection of resistant mutants, but that key determinants of resistance selection are the relationships between the number of drug-inactivated targets within a cell and the rates of cellular growth and death. We also show that heterogeneous drug-target binding within a population enables resistant bacteria to evolve fitness-improving secondary mutations even when drug doses remain above the resistant strain's minimum inhibitory concentration. Our work suggests that antibiotic doses beyond this "secondary mutation selection window" could safeguard against the emergence of high-fitness resistant strains during treatment.
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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.