Optimizing dosing to prevent antibiotic resistance

描述(申请人提供)：细菌中的抗生素耐药性是医学上的一个主要问题。人们一再呼吁谨慎使用抗生素，但对如何优化使用以保持疗效知之甚少。更好地理解剂量和抗性机制选择之间的关系可能是有用的。我们采取了一种方法，将药代动力学、细菌学和分子数据整合到一个药效学模型中，该模型检查了金黄色葡萄球菌在体外系统中接触环丙沙星时出现耐药性的情况。该系统可以准确模拟人体药代动力学，并监测细菌的药效作用。我们发现，抗生素“敏感”(S)的培养通常含有低水平耐药(RL)的亚群；提供低浓度抗生素的方案可能会杀死S，但允许RL存活，而不会演变为高耐药(Ru)的细菌；产生中等浓度的方案可能会根除S，但会导致RL通过多种机制演变为RH；而产生高浓度的方案可能会在S和RL菌株演变为RH之前将其消灭，因此，RL向RH的演变，最终治疗的成功或失败，似乎取决于药物。在一定程度上，取决于抗生素的剂量。初步的药效学模型很好地描述了实验数据。基于这些发现，我们假设新的方案可以防止耐药的出现，通过了解抗生素浓度对抗生素耐药机制选择的影响，可以合理地设计这些方案。为了验证这一假设，我们将在体外系统中将细菌暴露于恒定和波动的环丙沙星浓度，并使用常规检测和实时荧光聚合酶链式反应监测具有上调外排和/或拓扑异构酶基因喹诺酮耐药决定区突变的细菌的发生率和流行率。药代动力学参数和耐药机制之间的相关性将被用来开发替代的药效学模型，以更准确地表征剂量和耐药之间的关系。药效学模型预测旨在预防(或允许)出现耐药性的方案结果的能力将使用由不同比例的S、RL和RH细菌组成的人工构建的培养物进行测试。我们相信，了解耐药性的潜在机制将增强我们设计替代剂量策略以实现临床治愈的能力。

DESCRIPTION (provided by applicant): 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.