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

对新冠肺炎的脆弱性来自多种风险因素，但有两个因素尤为突出：缺乏对新型冠状病毒的抗体；以及肺粘液的结构特征。该项目通过有重点的实验和来自两个实验室的数据，通过数学建模、模拟和理论，解决了主动反馈环路中的风险因素。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新冠肺炎。基线模型将区分吸入病毒载量的清除与呼吸道感染的发生和传播。在感染进展阶段的基线模型的基础上，将模拟两种实验干预：吸入剂量的新冠肺炎特异性单抗(MAb)和结构靶向的粘液溶解药物。这两个实验室都将利用数学预测来加快和优化其缓解新冠肺炎疾病的策略。同样，实验结果和数据将被用来验证模型并学习对模型预测准确性至关重要的隐藏因素。该项目潜在的更广泛的影响是预测在新冠肺炎肺部感染的不同阶段针对特定亚群的最佳、基于单抗和基于粘液的治疗方法。数学建模平台将探索以下微妙的相互作用：新冠肺炎的吸入负荷，它们在粘液覆盖的呼吸道内扩散，并可能通过粘液覆盖的呼吸道；当新冠肺炎到达并入侵上皮细胞，产生侵入新细胞并传播感染的子代时，传染性开始；接触新冠肺炎后多长时间，天然抗体或基因工程单抗被引入粘液层；以及粘液层从肺部的清除率。众所周知，后一种清除率通常极大地取决于新冠肺炎易感人群中粘液的结构特性。实验团队将在非传染性新冠肺炎纳米颗粒上测试现有单抗的结果。一个多物种随机模型将模拟新冠肺炎的扩散和传播，新冠肺炎和粘蛋白之间的单抗交联会中断。一些反应扩散参数被测量，而另一些将使用隐马尔可夫方法从实验数据中学习。基于聚合物物理学的粘液分子动力学模型将根据已知特定亚群的粘蛋白聚合物的化学结构和浓度来模拟粘液的结构特性。在这个建模平台上，目标是：优化单抗设计；表征给定单抗与新冠肺炎和粘液的亲和力；以及量化在不同进展阶段阻止新冠肺炎感染所需的吸入单抗剂量，具体针对粘液结构属性。该赠款是使用冠状病毒援助、救济和经济安全法案提供的资金授予MPS的补充资金。该奖项反映了美国国家科学基金会的法定使命，并已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。

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.