Optimizing COVID-19 Vaccine Diffusion in Respiratory Mucosa through Stokes-Einstein Modeling

SARS-COV-2 vaccines, all of which are currently intramuscular shots, have the ability to prevent serious injury. However, the absence of sufficient mucosal immunity is a major concern. To counteract this deficiency that has led to continued transmission from vaccinated individuals and breakthrough cases, reformulating vaccines to be inhalable presents a logical administration route. Predecessor research has reported the inhalable route to be viable as aerosolized vaccine nanoparticles, AAV phage nanoparticles, and PIV-5 viruses were recently identified to elicit immune responses. In this study, the diffusion of vaccine nanoparticles across the mucosa is characterized and modeled, with respect to their observed behavior from previous studies in relation to the Stokes-Einstein equation, to predict the most efficient model of an inhalable COVID-19 vaccine. The Stokes-Einstein equation has been used in several studies to predict diffusion coefficients. These predictions may be modified to fit the specifications of mucosal interactions. It was determined that mucosal interactions play a significant role in vaccine nanoparticle diffusion, as demonstrated by the viral vector and virus-like nanoparticle diffusion, and can be characterized by an equivalent hydrodynamic radius. Moreover, as a counter to mucosal interactions, PEGylation was found to drastically decrease the viscous slowing of the mucus medium.