Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland.
Evolutionary Biology, Institute for Biology, Freie Universität Berlin, Berlin, Germany.
Microbiology (Reading). 2023 Jul;169(7). doi: 10.1099/mic.0.001368.
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.
药代动力学-药效动力学（PKPD）模型描述了药物浓度随时间的变化方式，以及这种变化如何影响病原体的生长，这些模型在设计旨在消灭细菌的最佳药物治疗方面非常有价值。然而，抗菌药物耐药性的迅速上升要求我们更加关注另一个治疗优化标准：避免耐药性的进化。我们在这里展示了如何结合 PKPD 和群体遗传学模型来确定治疗方案，以最大程度地减少抗菌药物耐药性进化的可能性。重要的是，所得到的建模框架使我们能够评估对动态选择压力（包括抗菌药物浓度的变化和适应性表型的出现）的耐药性进化。我们以抗生素和抗菌肽为例，讨论了各个模型参数背后的经验证据和直觉。我们进一步提出了该框架的几个扩展，这些扩展允许通过各种表型和遗传机制更全面和现实地预测细菌对抗微生物药物的逃逸。
