Lung microphysiological system for predicting antiviral drug efficacy for COVID-19

Development of physiologically relevant in vitro models that recapitulate key aspects of SARS-CoV-2 infections has proven challenging due to limitations associated with current New Approach Methodologies (NAMS). Most current NAMS suffer from limited throughput and fidelity, and are difficult to translate to high containment laboratories, resulting in heavy reliance on conventional immortalized cell culture systems and animal models for evaluation of antiviral efficacy, both of which struggle to accurately mimic human physiologic responses. To address this challenge, Draper has been developing NAMS platforms incorporating complex human primary cell-based tissue models that better replicate organ function and that are amenable to robust operation in a BSL-3 environment. Here we report on PREDICT96-ALI screening of a panel of SARS-CoV-2 antiviral compounds that have been evaluated in human clinical trials. These compounds have been selected to probe the ability of the PREDICT96-ALI system to better distinguish efficacious from non-efficacious compounds than existing preclinical models and alternative NAMS. We found that PREDICT96-ALI discriminates established therapeutics and clinically promising drugs (ensitrelvir and obeldesivir respectively) from compounds that do not show clinical efficacy (lopinavir/ritonavir), demonstrating the power of this platform as a tool to improve prediction of human efficacy of drugs before clinical translation. • A microphysiological system is applied to screening therapeutics for SARS-CoV-2. • Excellent correlation between our model and clinical outcomes is obtained. • Emerging therapeutic candidates for SARS-CoV-2 infections have been evaluated. • The platform has broad applicability for screening for other respiratory infections.