Predicting Clinical Outcomes of SARS-CoV-2 Drug Efficacy with a High-Throughput Human Airway Microphysiological System

Abstract

The average cost to bring a new drug from its initial discovery to a patient's bedside is estimated to surpass $2 billion and requires over a decade of research and development. There is a need for new drug screening technologies that can parse drug candidates with increased likelihood of clinical utility early in development in order to increase the cost-effectiveness of this pipeline. For example, during the COVID-19 pandemic, resources were rapidly mobilized to identify effective therapeutic treatments but many lead antiviral compounds failed to demonstrate efficacy when progressed to human trials. To address the lack of predictive preclinical drug screening tools, PREDICT96-ALI, a high-throughput (n = 96) microphysiological system (MPS) that recapitulates primary human tracheobronchial tissue,is adapted for the evaluation of differential antiviral efficacy of native SARS-CoV-2 variants of concern. Here, PREDICT96-ALI resolves both the differential viral kinetics between variants and the efficacy of antiviral compounds over a range of drug doses. PREDICT96-ALI is able to distinguish clinically efficacious antiviral therapies like remdesivir and nirmatrelvir from promising lead compounds that do not show clinical efficacy. Importantly, results from this proof-of-concept study track with known clinical outcomes, demonstrate the feasibility of this technology as a prognostic drug discovery tool.

Keywords: SARS‐CoV‐2; airway model; microphysiological system; organs‐on‐chip; respiratory virus.