Application of a high-content screening assay utilising primary human lung fibroblasts to identify antifibrotic drugs for rapid repurposing in COVID-19 patients

Abstract

Lung imaging and autopsy reports amongst COVID-19 patients show elevated lung scarring (fibrosis). Early data from COVID-19 patients as well as previous studies from Severe Acute Respiratory Syndrome, Middle East Respiratory Syndrome and other respiratory disorders show that the extent of lung fibrosis is associated with a higher mortality, prolonged ventilator dependence as well as a poorer long-term health prognosis. Current treatments to halt or reverse lung fibrosis are limited, thus the rapid development of effective antifibrotic therapies is a major global medical need that will continue far beyond the current COVID-19 pandemic. Reproducible fibrosis screening assays with high signal-to-noise and disease relevant readouts such as extracellular matrix (ECM) deposition (the hallmark of fibrosis), are integral to any antifibrotic therapeutic development. Therefore, we have established an automated high throughput and high content primary screening assay measuring TGFβ-induced ECM deposition from primary human lung fibroblasts in a 384 well format. This assay combines longitudinal live-cell imaging with multiparametric high content analysis of ECM deposition. Using this assay, we have screened a library of 2,743 small molecules representing approved drugs and late-stage clinical candidates. Confirmed hits were subsequently profiled through a suite of secondary lung fibroblast phenotypic screening assays quantifying cell differentiation, proliferation, migration and apoptosis. In silico target prediction and pathway network analysis was applied to the confirmed hits. We anticipate this suite of assays and data analysis tools will aid the identification of new treatments to mitigate against lung fibrosis associated with COVID-19 and other fibrotic diseases.

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This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).

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DOI

https://doi.org/10.1177/24725552211019405

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https://www.research.ed.ac.uk/en/publications/9bf47e89-d77d-43f8-8cd6-107ddddefb2e