Optimizing dosing to prevent antibiotic resistance

优化剂量以防止抗生素耐药性

• 批准号: 6508689
• 负责人: Martin E Evans
• 金额: $ 26.88万
• 依托单位: UNIVERSITY OF KENTUCKY
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6508689
• 关键词: DNA topoisomerases; Staphylococcus aureus; antibacterial agents; antibiotics; bacteria infection mechanism; bacterial genetics; disease /disorder prevention /control; dosage; drug resistance; drug screening /evaluation; fluorescence spectrometry; high performance liquid chromatography; pharmacokinetics; polymerase chain reaction; quinoline

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.

描述（由申请人提供）：细菌之间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。更好地了解给药与选择耐药机制之间的关系可能是有用的。我们采用了一种方法，将药代动力学、细菌学和分子数据整合到药效学模型中，在体外系统中检测金黄色葡萄球菌暴露于环丙沙星时耐药性的出现。该系统可以精确模拟人体药代动力学和监测对细菌的药效学影响。我们发现抗生素“敏感”(S)培养通常含有低水平耐药性（RL）的亚群；提供低抗生素浓度的方案可能会杀死S，但允许RL存活而不会进化成具有高耐药性的细菌（Ru）；产生中等浓度的方案可以根除S，但通过各种机制导致RL演变为RH；因此，RL向RH的演变，以及最终治疗的成功或失败，似乎是相互依赖的。部分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，我们假设新的治疗方案可以防止耐药性的出现，并且可以通过了解抗生素浓度对抗生素耐药机制选择的影响来合理设计这些方案。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中，并使用常规检测和实时PCR监测在拓扑异构酶基因的喹诺酮类耐药决定区域中外排上调和/或突变的细菌的发生率和流行程度。药代动力学参数与耐药机制之间的相关性将用于开发替代药效学模型，以更准确地描述剂量与耐药之间的关系。药效学模型预测旨在防止（或允许）耐药性出现的方案结果的能力将通过由不同比例的S、RL和RH细菌组成的人工构建培养物进行测试。我们相信，了解耐药性的潜在机制将增强我们设计替代给药策略的能力，从而实现临床治愈。