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

对COVID-19的脆弱性由多种风险因素引起，但有两个风险因素尤为突出：缺乏针对新型冠状病毒的抗体（Ab）；以及肺粘液的结构特性。该项目通过重点实验和两个实验室的数据，通过数学建模、模拟和理论，在一个主动反馈回路中解决这两个风险因素。这项数学工作将提供一个平台，从基线模型开始，模拟在没有抗体保护的情况下吸入COVID-19的暴露。基线模型将区分吸入病毒载量的清除与呼吸道感染的发病和传播。在感染进展阶段的基线模型基础上，将模拟两种实验性干预措施：吸入剂量的covid -19特异性单克隆抗体（mAb）和结构靶向黏液解药。这两个实验室将利用数学预测来加速和优化其缓解COVID-19疾病的策略。同样，将利用实验结果和数据来验证模型，并了解对模型预测准确性至关重要的隐藏因素。该项目潜在的更广泛影响是预测针对COVID-19肺部感染不同阶段的特定亚群的最佳、基于单克隆抗体和基于黏液溶解剂的治疗方法。该数学建模平台将探索以下因素之间的微妙相互作用：吸入的COVID-19负荷，它们在粘液覆盖的呼吸道内和可能通过呼吸道的扩散；随着COVID-19到达并侵入上皮细胞，产生子代侵入新细胞并传播感染，传染性开始发作；暴露于COVID-19后多久，天然抗体或工程单克隆抗体被引入黏液层；以及肺部黏液层的清除率。众所周知，后一种清除率通常在很大程度上取决于COVID-19易感亚群中黏液的结构特性。实验小组将测试现有的单抗对非传染性COVID-19纳米颗粒的效果。一个多物种随机模型将模拟COVID-19的扩散和传播，并被COVID-19与粘蛋白之间的单克隆抗体交联所中断。一些反应扩散参数是测量的，而其他的将使用隐马尔可夫方法从实验数据中学习。基于聚合物物理的黏液分子动力学模型将基于特定亚群已知的黏液聚合物的化学结构和浓度来模拟黏液的结构特性。利用该建模平台，目标是：优化mAb设计；表征给定单抗对COVID-19和黏液的亲和力；并量化在不同进展阶段阻止COVID-19感染所需的吸入单抗剂量，具体到粘液结构特性。这笔拨款是使用《冠状病毒援助、救济和经济安全（关怀）法案》分配给MPS的补充资金提供的资金。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。

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.