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.
