An interdisciplinary team from the University of North Carolina at Chapel Hill has received funding to explore the critical role of airway mucus in the transmission, infection and spread versus protection from COVID-19.
While the world waits for a vaccine against COVID-19, it is critical that we understand more about this novel virus in order to anticipate — and potentially mitigate — its spread.
UNC-Chapel Hill professors M. Gregory Forest from the mathematics department, Ronit Freeman from the applied physical sciences department and Samuel Lai from the Eshelman School of Pharmacy are focused on developing computational models that recapitulate the spread of COVID-19 infections in the lung over time — and that guide different experimental strategies to prevent and mitigate the infection by reinforcing the airway mucus barrier.
They have received a rapid response research grant from the Division of Mathematical Sciences of the National Science Foundation, in response to an urgent call for actionable responses to the COVID-19 crisis. The grant provides $200,000 for one year.
The research will be guided by mathematical modeling and simulations of virus transport through mucus, the polymer gel that lines the airway, based on directly measuring the real-time mobility of SARS-CoV-2 virus-like-particles in fresh, undiluted human airway mucus.
“Mechanistic simulations of how COVID-19 spreads through the respiratory tract is essential to understanding the disease pathophysiology, and also to designing and optimizing the tandem effect of synthetic antibodies and mucolytic drugs, specifically for at-risk populations,” said Forest, who will lead the mathematical theory and modeling effort. “This information will allow us to provide a scientific basis for targeted doses of medications to arrest and clear COVID-19 infection based on the degree of progression in the lung and any mucus compromises that cause high risk.”
The computational work will support experiments in the Lai and Freeman labs, which are exploring two complementary strategies to fight exposure to inhaled doses of COVID-19. The Lai lab has been developing a variety of muco-trapping monoclonal antibody candidates as inhaled immunotherapies against COVID-19. Lai’s team, supported by various sponsors, is anticipating its first antibody candidate will be fast-tracked to human studies before year’s end.
“Given the uncertainty surrounding how quickly a COVID-19 vaccine can be identified, produced, and made readily available, developing early interventions to treat patients to minimize hospitalization is critical,” said Lai. “This work builds on a longstanding collaboration between our groups, and we expect the mathematical predictions will help identify the time window and dosing of COVID-19-specific mAb to effectively reduce the spread of the infection within the lung.”
The Freeman lab is exploring the ways in which the SARS-CoV-2 virus takes advantage of mucus structure anomalies that increase vulnerability to infection.
“We currently do not know why some populations are more vulnerable to becoming infected,” said Freeman. “It is important to understand how compositional and structural changes in airway mucus might render one individual resistant, while the other is more at risk for viral infection,” she said.
Freeman and her team are looking into interactions of airway mucus from various sub-populations with non-hazardous COVID-19 strains. Guided by the Forest team simulations, they hope to link specific structural anomalies in airway mucus to susceptibility for infection and explore mucolytic drugs to repair and restore a more efficient mucus barrier for viral protection.
Together, Forest, Freeman and Lai aim to unravel the interplay between the virus, mucus and inhaled therapies to arrest COVID-19 infection at various stages of disease progression.
“We are grateful to the NSF Division of Mathematical Sciences for the opportunity to pursue our ideas and strategies, and in doing so, to hopefully make a meaningful contribution to this pandemic,” said Forest. The tools the team will develop are universal to inhaled pathogens and are therefore adaptable to mitigate emerging pathogens for future pandemics.