The success of antimicrobial treatment is threatened by the evolution of drug resistance. Population genetic models are an important tool in mitigating that threat. However, most such models consider resistance emergence via a single mutational step. Here, we assembled experimental evidence that drug resistance evolution follows two patterns: i) a single mutation, which provides a large MIC increase, or ii) multiple mutations, each conferring a small increase, which combine to yield high-level resistance. Using stochastic modeling we then investigated the consequences of these two patterns for treatment failure and population diversity under various treatments. We find that resistance evolution is substantially limited if more than two mutations are required and that the most efficacious drug type depends on the pharmacokinetic profile. Further, we demonstrate that, for resistance evolution in multiple steps, adaptive treatment, which only suppresses the bacterial population, is favored over aggressive treatment, which aims at eradication.

中文翻译：

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不同药物，药代动力学和治疗方案下的多步耐药与单步耐药的演变

耐药性的发展威胁着抗菌治疗的成功。种群遗传模型是缓解这种威胁的重要工具。但是，大多数此类模型都考虑通过单个突变步骤产生抗性。在这里，我们收集了实验证据，表明耐药性的演变遵循两种模式：i）一个单突变，导致MIC大幅增加，或ii）多个突变，每个突变均引起小的增加，这些突变共同产生了高水平的耐药性。然后，我们使用随机建模方法研究了这两种模式对各种治疗方式下治疗失败和人群多样性的影响。我们发现，如果需要两个以上的突变，耐药性的发展将受到很大的限制，并且最有效的药物类型取决于药代动力学。进一步，