The Impact of Drug-Drug Interactions on the Evolution of Antibiotic Resistance

描述（由申请人提供）：抗生素耐药菌的出现和快速传播是一个日益严重和紧迫的公共卫生问题。耐药细菌的存在迫使人们更加依赖多种药物治疗来对抗细菌病原体。目前对多药治疗的研究主要集中在细菌感染率和对患者的毒性作用上。然而，关于多药治疗如何影响耐药性的演变，更具体地说是突变体选择窗口（MSW）-被认为对耐药性具有选择性的药物浓度范围，人们知之甚少。本申请使用理论实验方法解决了这个问题，集中于金黄色葡萄球菌，其多药耐药性菌株引起了急性医学关注。申请的具体目的有三个。首先，将开发一种新的实验技术，用于系统测量多药与单药组合中的耐药频率和MSW。高通量成像技术和自动计算分析将允许详细研究一系列抗生素组合中耐药菌落的出现率。结果将用于确定不同药物相互作用类型（无相互作用、协同作用、拮抗作用）是否以及如何影响MSW的大小。其次，将构建一个理论框架和数学模型，以预测在多药治疗的基础上，在每一个单独的单一药物的自发耐药率。这产生了一个预测模型，可以与实际数据进行比较。实验部分（目标1）的结果将用于微调和评估模型。第三，直接检查多药物组合对耐药性演变速率的影响。人口S。将使用测量荧光标记群体与时间的比率的新技术来测定在一系列药物组合中进化的金黄色葡萄球菌的适应性增加。这一目标的结果将显示药物组合是否以及如何影响耐药性的适应率。总之，这项研究将深入了解多种药物如何影响MSW，进而影响耐药性的总体演变速度。与公共卫生的相关性：由于很少有新的抗生素被开发出来，更多的耐药病原体出现并迅速传播，了解药物治疗如何影响细菌病原体耐药性的演变越来越紧迫。使用新的理论模型和新的实验室技术，我建议研究不同的多药组合如何影响耐药性，以及如何利用这些信息来减缓多药耐药性的出现。

DESCRIPTION (provided by applicant): The emergence and rapid spread of antibiotic resistant bacteria is a growing and urgent public health concern. The presence of resistant bacteria has forced greater reliance on multi-drug treatments to combat bacterial pathogens. Current research on multi-drug treatments has focused on bacteria kill-rate and toxicity effects on patients. Very little is known, however, about how multi-drug treatments affect the evolution of resistance and more specifically the Mutant Selection Window (MSW) - the range of drug concentrations which is thought to be selective for resistance. This application tackles this question using a theoretical experimental approach, concentrating on Staphylococcus aureus, whose multi-drug resistance strains pose an acute medical concern. There are three specific aims of the application. First, a new experimental technique will be developed for systematic measurement of resistance frequencies and the MSW in multi-drug versus single drug combinations. High-throughput imaging techniques and automated computational analyses will allow detailed study of the rate of appearance of resistant colonies in a range of antibiotic combinations. The results will be used to determine whether and how different drug interaction types (no interaction, synergistic, antagonistic) affect the size of the MSW. Second, a theoretical framework and a mathematical model will be constructed to predict the rate of spontaneous resistance in multi-drug treatment based on this rate in each of the single drugs alone. This yields a predictive model against which actual data can be compared. Results from the experimental component (Aim 1) will be used to fine-tune and assess the model. Third, the impact of multi-drug combinations on the rate of evolution of resistance is examined directly. Populations of S. aureus evolving in a range of drug combinations will be assayed for their fitness increase using a new technique that measures ratios of fluorescently labeled populations versus time. Results from this aim will show whether and how drug combinations can affect the rate of adaptation of drug resistance. Together, this research will provide insight into how multi-drugs affect the MSW and in turn the overall rate of evolution of drug resistance. Relevance to public health: With few new antibiotics being developed and greater numbers of drug resistant pathogens emerging and spreading rapidly, there is an increasing urgency to understand how drug treatments affect the evolution of resistance in bacterial pathogens. Using new theoretical models and new laboratory techniques, I propose to study how different multi-drug combinations affect drug resistance and how this information can be used to slow the emergence of multi-drug resistance.