Steering Evolution with Sequential Therapy to Prevent the Emergence of Bacterial Antibiotic Resistance.
Steering Evolution with Sequential Therapy to Prevent the Emergence of Bacterial Antibiotic Resistance.
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DOI：
10.1371/journal.pcbi.1004493
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发表时间：
2015-09
影响因子：
4.3
通讯作者：
Scott JG
中科院分区：
文献类型：
--
作者：
Nichol D;Jeavons P;Fletcher AG;Bonomo RA;Maini PK;Paul JL;Gatenby RA;Anderson AR;Scott JG
抗生素耐药性的增加和新型抗生素治疗方法的缓慢发现对公共卫生构成了越来越大的威胁。在这里，我们考虑一个简单的模型的进化无性繁殖的人口，认为适应作为一个有偏见的随机行走的健身景观。该模型关联的全局属性的健身景观与马尔可夫链转移矩阵的代数性质，并允许我们得出一般结果的非交换性和不可逆性的自然选择以及抗生素循环策略。使用这种形式主义，我们分析了15个经验适应景观的E。在不同β-内酰胺类抗生素筛选下的大肠杆菌中，证明了对给定抗生素的耐药性的出现可以通过不同的药物应用顺序来阻碍或促进。具体来说，我们证明了大多数（约70%）使用2 - 4种药物的序贯药物治疗促进了对最终抗生素的耐药性。此外，我们推导出最佳的药物应用序列，我们可以通过基因型空间的概率“引导”人口，以避免出现耐药性。这表明在对抗耐药生物的战争中有一种新的策略：药物测序通过基因型空间引导进化到不能出现耐药性的状态，并通过这种状态最大限度地提高成功治疗的机会。抗生素耐药性的增加，再加上新抗生素制剂发现速度的减缓，是一个可能很快达到危机点的公共卫生威胁。事实上，在过去十年中，已经出现了致命的、高度耐药的病原体形式，例如大肠杆菌、鲍曼不动杆菌、肺炎克雷伯氏菌、肠球菌和金黄色葡萄球菌，以及包括疟疾在内的非细菌病原体和艾滋病毒等病毒。在这里，我们开发了一个进化的细菌种群的数学模型，这使我们能够预测耐药菌株出现的概率。使用这个模型，我们展示了如何顺序的药物可以规定，以防止耐药性，每种药物单独可能会失败。这些模型预测提出了一种新的治疗策略：使用抗生素序列来“引导”病原体的进化，使其对最终抗生素不会产生耐药性。此外，我们测试了当处方抗生素的任意序列时出现耐药性的可能性，发现大约70%的2 - 4种药物的任意序列促进了对最终药物的耐药性。这一结果是一个警示，我们可能会通过粗心（或随机）的药物处方无意中促进耐药性。
The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Here, we consider a simple model of evolution in asexually reproducing populations which considers adaptation as a biased random walk on a fitness landscape. This model associates the global properties of the fitness landscape with the algebraic properties of a Markov chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Using this formalism, we analyze 15 empirical fitness landscapes of E. coli under selection by different β-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be either hindered or promoted by different sequences of drug application. Specifically, we demonstrate that the majority, approximately 70%, of sequential drug treatments with 2–4 drugs promote resistance to the final antibiotic. Further, we derive optimal drug application sequences with which we can probabilistically ‘steer’ the population through genotype space to avoid the emergence of resistance. This suggests a new strategy in the war against antibiotic–resistant organisms: drug sequencing to shepherd evolution through genotype space to states from which resistance cannot emerge and by which to maximize the chance of successful therapy. Increasing antibiotic resistance, coupled with the slowing rate of discovery of novel antibiotic agents, is a public health threat which could soon reach crisis point. Indeed, the last decade has seen the emergence of deadly, highly resistant forms of pathogens, such as Escherichia coli, Acenitobacter baumanii, Klebsiella pneumoniae, Enterococcus and Staphylococcus aureus as well as non–bacterial pathogens including malaria and viruses such as HIV. Here, we develop a mathematical model of an evolving bacterial population, which allows us to predict the probability of resistant strains emerging. Using this model we show how sequences of drugs can be prescribed in order to prevent resistance where each drug alone may fail. These model predictions suggest a novel treatment strategy: using sequences of antibiotics to ‘steer’ the evolution of a pathogen to a configuration from which resistance to a final antibiotic cannot emerge. Further, we test the likelihood of resistance emerging when arbitrary sequences of antibiotics are prescribed, finding that approximately 70% of arbitrary sequences of 2–4 drugs promote resistance to the final drug. This result serves as a cautionary warning that we may be inadvertently promoting resistance through careless (or random) prescription of drugs.