RAPID: A Lung Mucus Strategy for COVID-19 Viral Protection

RAPID：针对 COVID-19 病毒防护的肺粘液策略

基本信息

批准号：
2028758
负责人：
M Forest
金额：
$ 20万
依托单位：
University of North Carolina at Chapel Hill
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-15 至 2021-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028758&HistoricalAwards=false
关键词：
RAPID Lung Mucus Strategy COVID

项目摘要

Vulnerability to COVID-19 arises from multiple risk factors, but two are particularly prominent: lack of antibodies (Ab) to the novel coronavirus; and structural properties of lung mucus. This project addresses both risk factors through focused experiments and data from two labs in an active feedback loop with mathematical modeling, simulations, and theory. The mathematical effort will provide a platform, starting from a baseline model and simulation of exposure to inhaled COVID-19 without Ab protection. The baseline model will distinguish clearance of the inhaled viral load versus onset and propagation of respiratory infection. Two experimental interventions will be simulated on top of the baseline model at progressive stages of infection: inhaled doses of COVID-19-specific monoclonal antibodies (mAb) and of structure-targeted mucolytics. Both labs will leverage mathematical predictions to accelerate and optimize their strategies to mitigate COVID-19 disease. Likewise, experimental results and data will be leveraged to validate models and learn hidden factors critical to the predictive accuracy of the models. The potential broader impact of this project is to predict optimal, mAb-based and mucolytic-based treatments for specific sub-populations at various stages of COVID-19 lung infection.The mathematical modeling platform will explore the delicate interplay among: inhaled loads of COVID-19, their diffusion within, and potentially through, the mucus-coated respiratory tract; infectivity onset as COVID-19 reaches and invades epithelial cells, produces daughters that invade new cells and propagate the infection; how long after exposure to COVID-19 either natural antibodies or engineered mAb are introduced into the mucus layer; and, the rate of clearance of the mucus layer from the lung. The latter clearance rate is known to depend, often dramatically, on structural properties of mucus in vulnerable sub-populations to COVID-19. The experimental team will test outcomes of existing mAb on non-infectious COVID-19 nanoparticles. A multi-species stochastic model will simulate diffusion and propagation of COVID-19, interrupted by mAb-crosslinks between COVID-19 and mucins. Some reaction-diffusion parameters are measured while others will be learned from experimental data using hidden Markov methods. A polymer-physics-based molecular dynamics model of mucus will simulate structure properties of mucus based on the chemical structure and concentrations of mucin polymers known for specific sub-populations. With this modeling platform, the goals are to: optimize mAb design; characterize efficiency of given mAb affinities to COVID-19 and mucus; and quantify the inhaled mAb dose required to arrest COVID-19 infection at various stages of progression, specific to mucus structure properties.This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplemental funds allocated to MPS.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.

Covid-19的脆弱性来自多种危险因素，但两个危险因素特别突出：缺乏对新型冠状病毒的抗体（AB）；肺粘液的结构特性。该项目通过重点实验和来自两个实验室的数据来解决风险因素，并通过数学建模，模拟和理论来解决主动反馈循环中的数据。数学工作将提供一个平台，从基线模型开始，并在没有AB保护的情况下对吸入Covid-19的接触模拟。基线模型将区分吸入病毒负荷与发作和呼吸道感染传播的清除。在感染的渐进阶段，将在基线模型的顶部模拟两种实验干预措施：吸入剂量的剂量，共证于19特异性单克隆抗体（MAB）和结构靶向的粘液溶液。这两个实验室都将利用数学预测来加速和优化其减轻Covid-19疾病的策略。同样，实验结果和数据将被利用以验证模型并学习对模型预测准确性至关重要的隐藏因素。该项目的潜在更广泛的影响是预测在Covid-19肺部感染的各个阶段的特定子人群的最佳，基于mab的和粘液溶剂的处理。数学建模平台将探索以下方面的微妙相互作用。 -19，它们在粘液涂层的呼吸道内部的扩散；随着COVID-19的到达并侵入上皮细胞，感染性发作会产生侵入新细胞并传播感染的女儿。暴露于Covid-19的多长时间后，将天然抗体或工程MAB引入粘液层；并且，肺部粘液层清除率。已知后一个清除率通常取决于易损坏的covid-19中粘液的结构特性。实验团队将测试现有MAB在非感染Covid-19纳米颗粒上的结果。多种物种随机模型将模拟Covid-19的扩散和传播，并被COVID-19和粘蛋白之间的mab-跨链接中断。测量了一些反应扩散参数，而其他反应扩散参数将使用隐藏的Markov方法从实验数据中学习。基于聚合物物理的粘液分子动力学模型将基于粘液结构和浓度的粘蛋白聚合物的浓度来模拟粘液的结构特性。使用此建模平台，目标是：优化MAB设计；表征给定的mAb亲和力对COVID-19和Mucus的效率；并量化在各个进展阶段逮捕Covid-19感染所需的吸入的单元剂量，特定于粘液结构的特性。该赠款是使用冠状病毒援助，救济和经济安全（CARES）ACT ACTENTION ACT ACTENTION ACT ACTENTION ACT ACT的资金授予的。分配给MP。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来评估值得支持的。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Learning from past failures: Challenges with monoclonal antibody therapies for COVID-19.

DOI：
10.1016/j.jconrel.2020.11.057
发表时间：
2021-01-10
期刊：
Journal of controlled release : official journal of the Controlled Release Society
影响因子：
0
作者：
Lai SK;McSweeney MD;Pickles RJ
通讯作者：
Pickles RJ

Chain stiffness boosts active nanoparticle transport in polymer networks

链刚度促进聚合物网络中活性纳米粒子的传输