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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

基本信息

批准号：
1814659
负责人：
Katharine Gurski
金额：
$ 20万
依托单位：
Howard University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1814659&HistoricalAwards=false
关键词：
Collaborative Research Linking Pharmacokinetics Epidemiological

项目摘要

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.

这项研究的目标是开发数学工具，以提高对药物在体内的行为及其对病原体的影响如何影响耐药疾病传播的理解。针对微生物的药物（抗菌剂）挽救了数百万人的生命。但是，由于微生物的进化，随着时间的推移，抗菌剂会减少那些屈服于药物的病原体的数量，而留下那些不屈服于药物的病原体--这一过程称为选择。因此，无节制地使用抗菌药物威胁到其长期疗效-自1928年发现青霉素以来一直令人担忧。今天，抗药性被认为是一个需要紧急关注的严重问题。微分方程可以描述驱动生命系统变化的机制;控制理论可以产生最佳策略，利用这些机制来实现预期目标。这些工具将有助于指导药物开发和给药方案，平衡施用抗菌药物的直接益处与这些药物施加的长期选择压力。一个适用于不同疾病系统的数学框架可以为减少耐药病原体对全球健康的威胁和随之而来的社会成本的战略提供信息。此外，该项目将为霍华德、德克萨斯理工大学、利哈伊和肯塔基州大学的研究生提供在洛斯阿拉莫斯国家实验室实习的机会，促进学术界以外的联系。肯塔基州大学的学生还将协助首席研究员通过一个试验性的星期六早晨数学项目指导高中生，该项目将提供编码、建模和科学结果可视化的实践经验。研究人员的目标是构建一个通用的数学框架，以病媒传播疾病为基准示例，并建立所需的工具，以弥合宿主内PK/PD（药代动力学/药效学）和人群水平流行病学之间的差距，以便其他人可以很容易地调整框架，以自己的研究竞争病原体。研究人员将通过在非线性常微分方程系统中引入连续区室来解决将PK/PD的快速动态与相对缓慢的群体水平动态联系起来的问题，该系统代表了个体通过不同药物浓度和不同持续时间的治疗阶段的进展。提出了一个随机子模型来参数化将宿主内PK/PD与群体水平动力学联系起来的函数之一。所提出的研究是重要的，因为到目前为止，还没有通用的方法来分析一个阶段有不同的持续时间的阶段进展模型。这种模型可以提供结果和见解，以减轻导致耐药性的选择压力的方式显着改善药物干预方案。例如，它是未知的可能存在的向后分叉和分期渐进的方法与异质阶段将如何相互作用，并影响最佳的治疗政策。此外，拟议的参数估计，不确定性和可识别性分析可能会导致具有挑战性的数学和统计问题，需要现有的方法的进步。该项目由数学科学部数学生物学计划和人力资源开发部HBCU-UP资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。