Collaborative Research: Linking Pharmacokinetics to Epidemiological Models of Vector-Borne Diseases and Drug Resistance Prevention

合作研究：将药代动力学与媒介传播疾病和耐药性预防的流行病学模型联系起来

• 批准号: 1953838
• 负责人: Olivia Prosper
• 金额: $ 6.57万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953838&HistoricalAwards=false
• 关键词: Collaborative; Research; Linking; Pharmacokinetics; Epidemiological

The goal of this research is to develop mathematical tools to improve understanding of how the behavior of drugs within the body, and their effects on pathogens, influence the spread of drug-resistant disease. Drugs that target microbes (antimicrobials) have saved millions of lives. But, because microbes evolve, over time antimicrobials reduce the population of pathogens that succumb to drugs and leave behind those that do not - a process called selection. Consequently, unbridled use of antimicrobials threatens their long-term efficacy - a concern since the discovery of penicillin in 1928. Today, drug resistance is recognized as a serious problem requiring urgent attention. Differential equations can describe mechanisms that drive changes in living systems; control theory can generate the best strategies to use these mechanisms to achieve a desired goal. These tools will help guide drug development and dosing protocols that balance the immediate benefits of administering antimicrobials with the long-term selection pressure imposed by these drugs. A mathematical framework that is adaptable to different disease systems could inform strategies to reduce the threat of resistant pathogens to global health and the attendant cost to society. Furthermore, this project will provide graduate students at Howard, Texas Tech, Lehigh, and the University of Kentucky, with the opportunity to intern at the Los Alamos National Laboratory, fostering connections outside of academia. University of Kentucky students will also assist the lead investigator in mentoring high school students through a pilot Saturday Morning Math program that will provide hands-on experience with coding, modeling, and visualization of scientific results.The investigators aim to construct a general mathematical framework, with vector-borne disease serving as a benchmark example, and build the tools needed to bridge the gap between within-host PK/PD (pharmacokinetics/pharmacodynamics) and population-level epidemiology, so that others may readily adapt the framework to their own studies of competing pathogens. The investigators will approach the problem of linking the fast dynamics of PK/PD to the comparatively slow population-level dynamics by introducing serial compartments in a system of nonlinear ordinary differential equations representing the progression of individuals through stages of treatment characterized by different drug concentrations and different durations. A stochastic sub-model is proposed to parameterize one of the functions that links within-host PK/PD to the population-level dynamics. The proposed research is important because, to date, no general methods exist to analyze a staged-progression model where the stages have different durations. Such a model could provide results and insights that significantly improve the protocols for drug interventions in a way that mitigates the selection pressure leading to drug resistance. For example, it is unknown how the likely existence of backward bifurcation and the staged-progression approach with heterogeneous stages will interact and influence optimal treatment policy. Furthermore, the proposed parameter estimation, uncertainty, and identifiability analyses will likely lead to challenging mathematical and statistical problems requiring advances of existing methodologies. This project is funded by the Division of Mathematical Sciences Mathematical Biology Program and Division of Human Resource Development HBCU-UP.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.

这项研究的目的是开发数学工具，以提高人们对体内药物的行为及其对病原体的影响的理解，影响耐药性疾病的传播。靶向微生物（抗菌剂）的药物挽救了数百万的生命。但是，由于微生物的发展，随着时间的流逝，抗菌素减少了屈服于药物的病原体的种群，并抛弃了那些不屈服的病原体 - 一种称为选择的过程。因此，对抗菌剂的无限使用威胁到其长期疗效 - 自1928年发现青霉素以来，这是一个关注的问题。如今，耐药性被认为是一个严重的问题，需要紧急关注。微分方程可以描述驱动生活系统变化的机制；控制理论可以生成最佳的策略来使用这些机制实现理想的目标。这些工具将有助于指导药物开发和给药方案，以平衡管理抗菌剂与这些药物施加的长期选择压力的直接益处。适用于不同疾病系统的数学框架可以为减少抗性病原体对全球健康的威胁和社会的伴随成本提供信息。此外，该项目将为霍华德，德克萨斯理工学院，利哈伊和肯塔基大学提供研究生，并有机会在洛斯阿拉莫斯国家实验室实习，从而在学术界范围内建立联系。 University of Kentucky students will also assist the lead investigator in mentoring high school students through a pilot Saturday Morning Math program that will provide hands-on experience with coding, modeling, and visualization of scientific results.The investigators aim to construct a general mathematical framework, with vector-borne disease serving as a benchmark example, and build the tools needed to bridge the gap between within-host PK/PD (pharmacokinetics/pharmacodynamics) and population-level流行病学，以便其他人可以轻松地将框架适应自己对竞争病原体的研究。研究人员将通过在非线性普通微分方程的系统中引入串行隔室，从而将PK/PD的快速动力学与相对较慢的人口级动力学联系起来，从而将其连接起来，该问题将代表个体通过不同药物浓度和不同持续时间的治疗阶段引入。提出了一个随机子模型来参数将宿主PK/PD内部与人群级动力学联系起来的功能之一。 拟议的研究很重要，因为迄今为止，尚无一般方法来分析阶段具有不同持续时间的分阶段模型。这样的模型可以提供结果和见解，从而显着改善药物干预方案，从而减轻导致耐药性的选择压力。例如，尚不清楚向后分叉的可能存在与异质阶段的分阶段进展如何相互作用并影响最佳治疗政策。此外，提出的参数估计，不确定性和可识别性分析可能会导致挑战性的数学和统计问题，需要现有方法的进步。 该项目是由数学科学生物学计划和人力资源开发部HBCU-UP的部门资助的。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准来通过评估来支持的。

项目成果

A Mosquito-Borne Disease Model with Non-exponentially Distributed Infection and Treatment Stages

具有非指数分布感染和治疗阶段的蚊媒疾病模型

• DOI: 10.1007/s10884-020-09863-2
• 发表时间: 2020
• 期刊: Journal of Dynamics and Differential Equations
• 影响因子: 1.3
• 作者: Feng, Z.;Gurski, K. F.;Prosper, O.;Teboh-Ewungkem, M. I.;Grogan, M.
• 通讯作者: Grogan, M.

Modeling Seasonal Malaria Transmission: A Methodology Connecting Regional Temperatures to Mosquito and Parasite Developmental Traits

季节性疟疾传播建模：将区域温度与蚊子和寄生虫发育特征联系起来的方法

• DOI: 10.30707/lib10.1.1682014077.793816
• 发表时间: 2023
• 期刊: Letters in Biomathematics
• 作者: Prosper, Olivia;Gurski, Katharine;Teboh-Ewungkem, Miranda I.;Peace, Angela;Feng, Zhilan;Reynolds, Margaret;Manore, Carrie
• 通讯作者: Manore, Carrie