Department of Evolutionary Theory, Research Group Stochastic Evolutionary Dynamics, Max Planck Institute for Evolutionary Biology, Plön, Germany.
J R Soc Interface. 2023 Jan;20(198):20220793. doi: 10.1098/rsif.2022.0793. Epub 2023 Jan 4.
Laboratory experiments suggest that rapid cycling of antibiotics during the course of treatment could successfully counter resistance evolution. Drugs involving collateral sensitivity could be particularly suitable for such therapies. However, the environmental conditions differ from those . One key difference is that drugs can be switched abruptly in the laboratory, while in the patient, pharmacokinetic processes lead to changing antibiotic concentrations including periods of dose overlaps from consecutive administrations. During such overlap phases, drug-drug interactions may affect the evolutionary dynamics. To address the gap between the laboratory and potential clinical applications, we set up two models for comparison-a 'laboratory model' and a pharmacokinetic-pharmacodynamic 'patient model'. The analysis shows that in the laboratory, the most rapid cycling suppresses the bacterial population always at least as well as other regimens. For patient treatment, however, a little slower cycling can sometimes be preferable if the pharmacodynamic curve is steep or if drugs interact antagonistically. When resistance is absent prior to treatment, collateral sensitivity brings no substantial benefit unless the cell division rate is low and drug cycling slow. By contrast, drug-drug interactions strongly influence the treatment efficiency of rapid regimens, demonstrating their importance for the optimal choice of drug pairs.
实验室实验表明,在治疗过程中快速循环使用抗生素可以成功地对抗耐药性进化。涉及附带敏感性的药物特别适合这种治疗。然而,环境条件与实验室不同。一个关键的区别是,药物可以在实验室中突然切换,而在患者中,药代动力学过程导致抗生素浓度变化,包括连续给药的剂量重叠期。在这种重叠阶段,药物相互作用可能会影响进化动态。为了弥合实验室和潜在临床应用之间的差距,我们建立了两个模型进行比较——一个是“实验室模型”,另一个是“药代动力学-药效学患者模型”。分析表明,在实验室中,最快的循环速度至少可以像其他方案一样抑制细菌种群。然而,对于患者治疗,如果药效曲线陡峭或药物相互拮抗,稍微较慢的循环有时可能更好。如果在治疗前不存在耐药性,那么附带敏感性没有实质性好处,除非细胞分裂速度低且药物循环速度慢。相比之下,药物相互作用强烈影响快速方案的治疗效率,表明它们对于最佳药物配对选择的重要性。