Antibiotic resistance among bacteria is a major problem in medicine. There have been repeated calls for the prudent use of antibiotics, but little is known about optimizing use to conserve efficacy. A better understanding of the relationship between dosing and the selection of resistance mechanisms may be useful. We have taken an approach that integrates pharmacokinetic, bacteriological, and molecular data into a pharmacodynamic model that examines the emergence of resistance when Staphylococcus aureus is exposed to ciprofloxacin in an in vitro system. This system allows accurate simulations of human pharmacokinetics and monitoring of the pharmacodynamic effect on bacteria. We found that antibiotic """"""""sensitive"""""""" (S) cultures often harbor subpopulations with low-level resistance (RL); regimens providing low antibiotic concentrations may kill S, but allow RL to survive without evolving into bacteria with high-level resistance (Ru); regimens producing moderate concentrations may eradicate S, but cause RL to evolve into RH through a variety of mechanisms; and regimens producing high concentrations may eradicate S and RL strains before they evolve into RH Thus, the evolution of RL to RH, and ultimately treatment success or failure, appears to be dependent. in part, upon antibiotic dosing. A preliminary pharmacodynamic model described the experimental data well. Based on these findings, we hypothesize that novel regimens may prevent the emergence of resistance, and these regimens can be rationally designed by understanding the effect of antibiotic concentrations on the selection of antibiotic resistance mechanisms. To test this hypothesis, we will expose bacteria to constant and fluctuating ciprofloxacin concentrations in the in vitro system and monitor the incidence and prevalence of bacteria with up-regulated efflux and/or mutations in the quinolone resistance determining regions of topoisomerase genes with conventional assays and real-time PCR. Correlations between phannacokinetic parameters and resistance mechanisms will be used to develop alternative pharmacodynamic models that more accurately characterize the relationship between dosing and resistance. The ability of the pharmacodynamic models to predict the outcome of regimens designed to prevent (or allow) the emergence of resistance will be tested using artificially constructed cultures comprised of varying proportions of S, RL, and RH bacteria. We believe understanding the mechanisms underlying resistance will enhance our ability to design alternative dosing strategies to effect clinical cure.
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