In Silico Prototyping for Intranasally Administered Agents for COVID-19 and Other Respiratory Pathogens

For respiratory pathogens, such as SARS-CoV-2, a dominant early infection site is the nasopharynx. Antivirals administered directly to this site are likely to have a broader therapeutic window than systemically administered agents. To account for the dearth of patient data from various demographics, we propose a machine learning-enabled protocol to identify optimal formulation design parameters that can be matched to nasal spray device parameters for effective drug delivery. For that, we have measured 11 anatomical parameters (e.g., nasopharyngeal volume, nostril heights) for ten representative CT-based nasal geometries. We have also performed 160 CFD simulations of drug delivery for a range of breathing conditions (by applying varying pressure gradients driving the inhaled air transport) on the same geometries to determine drug deposition at the nasopharynx for nasal inhalers. With this test set, a proof-of-concept machine learning model is being developed to quantify targeted drug delivery in a wider demographic, as a correlative function of upper airway geometric variations. This work contributes to the design of a personalized, efficient intranasal delivery modality for prophylactics, therapeutics, and vaccines; the results will find use in a variety of respiratory diseases.