Antibiotic Cycling and Antibiotic Mixing: Which One Best Mitigates Antibiotic Resistance?
Antibiotic Cycling and Antibiotic Mixing: Which One Best Mitigates Antibiotic Resistance?
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DOI：
10.1093/molbev/msw292
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发表时间：
2017-04-01
影响因子：
10.7
通讯作者：
Iredell J
中科院分区：
文献类型：
--
作者：
Beardmore RE;Peña-Miller R;Gori F;Iredell J
我们能否利用我们对分子进化的迅速理解来减缓耐药性的发展？感染临床医生的一个角色就是：在抗生素治疗期间预见耐药性的轨迹，并阻止这种演变过程。但这能在医院范围内实现吗？临床医生和理论家试图提出两种相互冲突的行为策略，这些策略被称为“抗生素循环”和“抗生素混合”，预计将在临床上遏制耐药性演变。然而，来自临床试验的累积数据，现在接近400万患者日的治疗，对于循环或混合来说变化太大，不能被视为成功。前者在医院的不同时间实施不同抗生素的限制和优先顺序，其方式据说是在它们之间“循环”。在抗生素混合中，将适当的抗生素随机分配给患者。混合的结果没有相关性，在时间或患者之间，在用于治疗的药物，这就是为什么理论家认为这是一个最佳的行为策略。因此，虽然循环和混合被提出作为控制进化的方式，但我们表明临床数据集不能在它们之间进行选择是有充分理由的：通过重新检查理论文献，我们表明先前对混合理论最优性的支持是错误的。我们的分析与数据中出现的模式一致：在减轻临床抗生素耐药性的选择方面，循环或混合都不是先验的。 关键词：抗生素循环，抗生素混合，最优控制，随机模型。
Can we exploit our burgeoning understanding of molecular evolution to slow the progress of drug resistance? One role of an infection clinician is exactly that: to foresee trajectories to resistance during antibiotic treatment and to hinder that evolutionary course. But can this be done at a hospital-wide scale? Clinicians and theoreticians tried to when they proposed two conflicting behavioral strategies that are expected to curb resistance evolution in the clinic, these are known as “antibiotic cycling” and “antibiotic mixing.” However, the accumulated data from clinical trials, now approaching 4 million patient days of treatment, is too variable for cycling or mixing to be deemed successful. The former implements the restriction and prioritization of different antibiotics at different times in hospitals in a manner said to “cycle” between them. In antibiotic mixing, appropriate antibiotics are allocated to patients but randomly. Mixing results in no correlation, in time or across patients, in the drugs used for treatment which is why theorists saw this as an optimal behavioral strategy. So while cycling and mixing were proposed as ways of controlling evolution, we show there is good reason why clinical datasets cannot choose between them: by re-examining the theoretical literature we show prior support for the theoretical optimality of mixing was misplaced. Our analysis is consistent with a pattern emerging in data: neither cycling or mixing is a priori better than the other at mitigating selection for antibiotic resistance in the clinic. Key words: antibiotic cycling, antibiotic mixing, optimal control, stochastic models.