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大剂量抗菌化疗能否预防耐药性的产生?

Does High-Dose Antimicrobial Chemotherapy Prevent the Evolution of Resistance?

作者信息

Day Troy, Read Andrew F

机构信息

Department of Mathematics and Statistics, Jeffery Hall, Queen's University, Kingston, Ontario, Canada.

Department of Biology, Queen's University, Kingston, Ontario, Canada.

出版信息

PLoS Comput Biol. 2016 Jan 28;12(1):e1004689. doi: 10.1371/journal.pcbi.1004689. eCollection 2016 Jan.

Abstract

High-dose chemotherapy has long been advocated as a means of controlling drug resistance in infectious diseases but recent empirical studies have begun to challenge this view. We develop a very general framework for modeling and understanding resistance emergence based on principles from evolutionary biology. We use this framework to show how high-dose chemotherapy engenders opposing evolutionary processes involving the mutational input of resistant strains and their release from ecological competition. Whether such therapy provides the best approach for controlling resistance therefore depends on the relative strengths of these processes. These opposing processes typically lead to a unimodal relationship between drug pressure and resistance emergence. As a result, the optimal drug dose lies at either end of the therapeutic window of clinically acceptable concentrations. We illustrate our findings with a simple model that shows how a seemingly minor change in parameter values can alter the outcome from one where high-dose chemotherapy is optimal to one where using the smallest clinically effective dose is best. A review of the available empirical evidence provides broad support for these general conclusions. Our analysis opens up treatment options not currently considered as resistance management strategies, and it also simplifies the experiments required to determine the drug doses which best retard resistance emergence in patients.

摘要

长期以来,高剂量化疗一直被视为控制传染病耐药性的一种手段,但最近的实证研究开始对这一观点提出挑战。我们基于进化生物学原理,开发了一个非常通用的框架来建模和理解耐药性的出现。我们使用这个框架来展示高剂量化疗如何引发相反的进化过程,这些过程涉及耐药菌株的突变输入及其从生态竞争中的释放。因此,这种疗法是否提供了控制耐药性的最佳方法取决于这些过程的相对强度。这些相反的过程通常会导致药物压力与耐药性出现之间呈现单峰关系。结果,最佳药物剂量处于临床可接受浓度治疗窗口的两端。我们用一个简单的模型来说明我们的发现,该模型展示了参数值看似微小的变化如何将结果从高剂量化疗最佳的情况改变为使用最小临床有效剂量最佳的情况。对现有实证证据的回顾为这些一般性结论提供了广泛支持。我们的分析开辟了目前未被视为耐药性管理策略的治疗选择,同时也简化了确定最能延缓患者耐药性出现的药物剂量所需的实验。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7d7/4731197/e18c65c75ec6/pcbi.1004689.g001.jpg

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