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.

National Science Foundation (NSF)
Division of Mathematical Sciences (DMS)
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Junping Wang
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University of North Carolina Chapel Hill
Chapel Hill
United States
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