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Modeling, Assessing, and Comparing Treatment Protocols to Prevent and Control Antibiotic Resistance

建模、评估和比较预防和控制抗生素耐药性的治疗方案

基本信息

批准号：
2052648
负责人：
Xi Huo
金额：
$ 28万
依托单位：
University of Miami
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2025-07-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2052648&HistoricalAwards=false
关键词：
Modeling Assessing Comparing Treatment Protocols

项目摘要

The emergence and spread of antimicrobial resistance are considered one of the biggest threats to human health in the 21st century. Bacteria can develop defense mechanisms against antibiotics, and the misuse and overuse of antibiotics are accelerating this process; all antibiotics have lost effectiveness against their targeted bacteria as of now. The evolutionary mechanisms for bacteria developing resistance to antibiotics are multifactorial and differ widely with the bacterial species, antibiotic classes and generations, route of administration, and the specific antibiotic-bacteria combination. This project will combine experimental data and mathematical models to quantify and simulate treatment dynamics for specific antibiotic-bacteria pairs. The primary objective is to seek optimal antibiotic use protocols on the patient level and validate theoretical conclusions with experiments. The results will improve current understanding of antibiotic use strategies and help design an integrated plan for future antimicrobial stewardship programs. This project will engage undergraduate and graduate students with interests in interdisciplinary research from both the main and medicine campuses of the University of Miami.The first goal of the project is to develop models that reflect the resistance development mechanism for specific antibiotic-bacteria combinations. In vitro experimental data will be generated and linked with the models to estimate the bacterial growth and evolutionary parameters under different antibiotic concentrations. Second, considering the immune response and the periodical drug concentrations, within-host bacterial dynamic models will be developed with periodic coefficients. The investigators will study the nonlinear dynamics of these models and evaluate the effects of four major antibiotic protocols: (i) mono-drug therapy, (ii) combination therapy, (iii) sequential cycling therapy, and (iv) de-escalation therapy. Third, the investigators will develop novel bacterial population models formulated in terms of semilinear partial differential equations and structured with respect to two physiological features: bacterial age and plasmid copies. The analysis of these models will advance understanding of drug resistance development at the cellular level and will establish new mathematical results on partial differential equations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

抗菌素耐药性的出现和传播被认为是21世纪对人类健康的最大威胁之一。细菌可以形成针对抗生素的防御机制，抗生素的误用和过度使用正在加速这一进程；到目前为止，所有抗生素对目标细菌都失去了效力。细菌对抗生素产生耐药性的进化机制是多因素的，并且随着细菌种类、抗生素类别和世代、给药途径以及特定的抗生素-细菌组合而有很大差异。该项目将结合实验数据和数学模型来量化和模拟特定抗生素-细菌对的治疗动态。主要目标是在患者层面寻求最佳的抗生素使用方案，并通过实验验证理论结论。研究结果将提高目前对抗生素使用策略的理解，并有助于为未来的抗菌药物管理项目设计一个综合计划。该项目将吸引来自迈阿密大学主校区和医学院对跨学科研究感兴趣的本科生和研究生。该项目的第一个目标是建立反映特定抗生素-细菌组合的耐药性发展机制的模型。将生成体外实验数据并与模型相关联，以估计不同抗生素浓度下细菌的生长和进化参数。其次，考虑到免疫反应和周期性药物浓度，将建立具有周期性系数的宿主内细菌动态模型。研究人员将研究这些模型的非线性动力学，并评估四种主要抗生素方案的效果：(i)单药治疗，（ii）联合治疗，（iii）顺序循环治疗，（iv）降级治疗。第三，研究人员将根据半线性偏微分方程建立新的细菌种群模型，并根据细菌年龄和质粒拷贝两种生理特征构建模型。这些模型的分析将在细胞水平上促进对耐药性发展的理解，并将在偏微分方程上建立新的数学结果。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。