ArticlePLoS computational biology2022
Modelling the synergistic effect of bacteriophage and antibiotics on bacteria: Killers and drivers of resistance evolution.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Mapping the landscape of mathematical models for antimicrobial resistance: a scoping review.BMC public health · 2026Article
- Bacteriophage therapy for multidrug-resistant bacterial infections: recent advances, clinical evidence, and translational challenges.Archives of microbiology · 2026Review
- Gradient metapopulation microfluidic ecologies shape genetic and biofilm drivers of T4r phage resistance in E. coli.NPJ biofilms and microbiomes · 2026Article
- Assessing the impact of bacterial heterogeneity on bacteriophage population dynamics.Journal of pharmacokinetics and pharmacodynamics · 2026Article
- Live and let die: Lysis time variability and resource limitation shape lytic bacteriophage fitness.PLoS computational biology · 2026Article
- Receptor identification and in vivo efficacy of a lytic phage vB_EcoStr-FJ63A against colistin-resistant Escherichia coli.Veterinary research · 2026Article
- Article
- The Respiratory Tract Microbiome and Human Health.Microbial biotechnology · 2025Review
- Piezoelectric biosensor with dissipation monitoring enables the analysis of bacterial lytic agent activity.Scientific reports · 2025Article
- CombatingFrontiers in cellular and infection microbiology · 2025Review
- A blueprint for broadly effective bacteriophage-antibiotic cocktails against bacterial infections.Nature communications · 2024Article
- Microfluidic Ecology Unravels the Genetic and Ecological Drivers of T4r Bacteriophage Resistance in E. coli: Insights into Biofilm-Mediated Evolution.Research square · 2024Article
- Current examining methods and mathematical models of horizontal transfer of antibiotic resistance genes in the environment.Frontiers in microbiology · 2024Review
- Characterization of novel bacteriophage PSKP16 and its therapeutic potential against β-lactamase and biofilm producer strain of K2-Hypervirulent Klebsiella pneumoniae pneumonia infection in mice model.BMC microbiology · 2023Article
- Article
- Structural Identifiability and Observability of Microbial Community Models.Bioengineering (Basel, Switzerland) · 2023Article
- Quantifying patient- and hospital-level antimicrobial resistance dynamics inmedRxiv : the preprint server for health sciences · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
Bacteriophage (phage) are bacterial predators that can also spread antimicrobial resistance (AMR) genes between bacteria by generalised transduction. Phage are often present alongside antibiotics in the environment, yet evidence of their joint killing effect on bacteria is conflicted, and the dynamics of transduction in such systems are unknown. Here, we combine in vitro data and mathematical modelling to identify conditions where phage and antibiotics act in synergy to remove bacteria or drive AMR evolution. We adapt a published model of phage-bacteria dynamics, including transduction, to add the pharmacodynamics of erythromycin and tetracycline, parameterised from new in vitro data. We simulate a system where two strains of Staphylococcus aureus are present at stationary phase, each carrying either an erythromycin or tetracycline resistance gene, and where multidrug-resistant bacteria can be generated by transduction only. We determine rates of bacterial clearance and multidrug-resistant bacteria appearance, when either or both antibiotics and phage are present at varying timings and concentrations. Although phage and antibiotics act in synergy to kill bacteria, by reducing bacterial growth antibiotics reduce phage production. A low concentration of phage introduced shortly after antibiotics fails to replicate and exert a strong killing pressure on bacteria, instead generating multidrug-resistant bacteria by transduction which are then selected for by the antibiotics. Multidrug-resistant bacteria numbers were highest when antibiotics and phage were introduced simultaneously. The interaction between phage and antibiotics leads to a trade-off between a slower clearing rate of bacteria (if antibiotics are added before phage), and a higher risk of multidrug-resistance evolution (if phage are added before antibiotics), exacerbated by low concentrations of phage or antibiotics. Our results form hypotheses to guide future experimental and clinical work on the impact of phage on AMR evolution, notably for studies of phage therapy which should investigate varying timings and concentrations of phage and antibiotics.
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