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Volume 169, Issue 7

The pharmacokinetic–pharmacodynamic modelling framework as a tool to predict drug resistance evolution Open Access

This article is part of the Microbial Evolution collection.

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.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): antimicrobial resistance evolution , dynamic selection pressure , PKPD modelling and population genetics
Funding
This study was supported by the:
  • Eidgenössische Technische Hochschule Zürich (Award 19-2-FEL-74)
    • Principle Award Recipient: ClaudiaIgler
  • Volkswagen Foundation (Award 96517)
    • Principle Award Recipient: NotApplicable
© 2023 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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2023-07-31
2024-05-17
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The pharmacokinetic–pharmacodynamic modelling framework as a tool to predict drug resistance evolution
Microbiology 169, 001368 (2023); https://doi.org/10.1099/mic.0.001368
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