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

抗菌素耐药性的出现和蔓延被认为是21世纪人类健康的最大威胁之一。细菌可以形成抵抗抗生素的防御机制，而抗生素的误用和过度使用正在加速这一过程；到目前为止，所有抗生素对目标细菌都已经失去了效力。细菌对抗生素产生耐药性的进化机制是多因素的，并且因细菌种类、抗生素类别和世代、给药途径以及特定的抗生素-细菌组合而有很大不同。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-细菌对的治疗动力学。主要目标是在患者水平上寻求最佳的抗生素使用方案，并通过实验验证理论结论。这一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计划设计一个综合计划。这个项目将吸引来自迈阿密大学主校区和医学校区的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开发反映特定抗生素-细菌组合的耐药性发展机制的模型。体外实验数据将被生成并与模型相关联，以估计不同抗生素浓度下的细菌生长和进化参数。其次，考虑免疫反应和周期性药物浓度，建立具有周期系数的宿主内细菌动力学模型。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)单一药物治疗，(Ii)联合治疗，(Iii)序贯循环治疗，和(Iv)降级治疗。第三，研究人员将开发新的细菌种群模型，该模型以半线性偏微分方程式为基础，并根据两个生理特征：细菌年龄和质粒拷贝来构建。对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，并将在偏微分方程式上建立新的数学结果。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.