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Efficacy for COVID-19 treatments can be enhanced significantly throughtargeting the nasopharynx, which has been shown to be the dominant preliminaryinfection site for SARS-CoV-2. Although intranasal drugs can be administeredeasily through drops or sprays, it is difficult to test whether currentprotocols will deliver the right amount of the drug to this locationconsistently. We are interested in developing an in silico prototyping tool torapidly identify optimal parameters for intranasal delivery. In this study, wehave applied computational fluid dynamics to simulate fluid flow through thenasal cavity and examined particle deposition for a drug formulation, mimickingdifferent delivery methods. The nasal geometry models were derived usingdigitized and meshed computed tomography (CT) scans of human patients. Usingthe nasal geometries, we simulated two different airflows: a laminar model at15 LPM (Liters/min) that simulated resting breathing rate and a Large EddySimulation (LES) model used to achieve a higher flow rate of 30 LPM. We wereable to run particle tracking simulations for these two airflow schemes to testdifferent drug properties such as particle size. The different injectionmethods used include surface injection which best replicates an inhaler-basedrelease of particle droplets into the nostril and the cone injection methodwhich best replicates a spray into the nostril. The results of the studysuggest that the most optimal drug particle size for targeting the intranasalinfection sites is around 6-14 microns.

Identifying the optimal parameters for sprayed and inhaled drug particulates for intranasal targeting of SARS-CoV-2 infection sites