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

描述(由申请人提供)：抗生素耐药细菌的出现和迅速传播是一个日益增长和紧迫的公共卫生问题。耐药细菌的存在迫使人们更多地依赖多种药物治疗来对抗细菌病原体。目前对多种药物治疗的研究主要集中在细菌杀灭率和对患者的毒性效应。然而，人们对多种药物治疗如何影响耐药性的演变，尤其是突变选择窗口(MSW)--被认为对耐药性具有选择性的药物浓度范围--的影响知之甚少。该应用程序使用理论实验方法解决了这个问题，集中在金黄色葡萄球菌身上，金黄色葡萄球菌的多重耐药菌株构成了一个严重的医学问题。该应用程序有三个具体目标。首先，将开发一种新的实验技术，用于系统地测量多种药物与单一药物组合中的耐药性频率和MSW。高通量成像技术和自动化计算分析将使人们能够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的药物相互作用类型(无相互作用、协同作用、拮抗作用)是否以及如何影响城市生活垃圾的大小。其次，将构建一个理论框架和数学模型来预测多药联合治疗中的自发耐药率，该比率仅基于每种单药的自发耐药率。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)的结果将用于对模型进行微调和评估。第三，直接考察了多药联合对耐药进化速度的影响。在一系列药物组合中进化的金黄色葡萄球菌种群将使用一种新技术来分析它们的适合度增加，该技术测量荧光标记种群的比率与时间的关系。这一目标的结果将显示药物组合是否以及如何影响耐药适应速度。总之，这项研究将深入了解多种药物如何影响城市生活垃圾，进而了解耐药的总体进化速度。与公共卫生的相关性：由于几乎没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迫切。利用新的理论模型和新的实验室技术，我提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.