ArticleMicrobiology (Reading, England)2023
The pharmacokinetic-pharmacodynamic modelling framework as a tool to predict drug resistance evolution.
Article in Microbiology (Reading, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A network dynamical simulation model for the study of antibiotic resistance in microbial communities.Gut microbes · 2026Article
- Pharmacokinetics determine persister formation inJAC-antimicrobial resistance · 2026Article
- Modelling the role of the microbiome in antimicrobial resistance across scales.Nature microbiology · 2026Review
- Bacterial Persister Cells as Evolutionary Catalysts of Antibiotic Resistance: Mechanisms, Clinical Implications, and Therapeutic Strategies.Antibiotics (Basel, Switzerland) · 2026Review
- Pharmacokinetic/pharmacodynamic relationship of a novel pleuromutilin derivative APTM against Mycoplasma gallisepticum.Poultry science · 2026Article
- Evolutionary digital twin framework for optimal aminoglycoside dosing in neonates with suspected sepsis.NPJ digital medicine · 2026Article
- Ecological and evolutionary mechanisms driving within-patient emergence of antimicrobial resistance.Nature reviews. Microbiology · 2024Review
- The evolution of antimicrobial peptide resistance in Pseudomonas aeruginosa is severely constrained by random peptide mixtures.PLoS biology · 2024Article
- Gene expression tradeoffs determine bacterial survival and adaptation to antibiotic stress.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
4 authors.
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Abstract
Pharmacokinetic-pharmacodynamic (PKPD) models, which describe how drug concentrations change over time and how that affects pathogen growth, have proven highly valuable in designing optimal drug treatments aimed at bacterial eradication. However, the fast rise of antimicrobial resistance calls for increased focus on an additional treatment optimization criterion: avoidance of resistance evolution. We demonstrate here how coupling PKPD and population genetics models can be used to determine treatment regimens that minimize the potential for antimicrobial resistance evolution. Importantly, the resulting modelling framework enables the assessment of resistance evolution in response to dynamic selection pressures, including changes in antimicrobial concentration and the emergence of adaptive phenotypes. Using antibiotics and antimicrobial peptides as an example, we discuss the empirical evidence and intuition behind individual model parameters. We further suggest several extensions of this framework that allow a more comprehensive and realistic prediction of bacterial escape from antimicrobials through various phenotypic and genetic mechanisms.
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