A SARS-CoV-2 cytopathicity dataset generated by high-content screening of a large drug repurposing collection

Author:

Ellinger Bernhard,Bojkova Denisa,Zaliani Andrea,Cinatl JindrichORCID,Claussen Carsten,Westhaus Sandra,Keminer OliverORCID,Reinshagen JeanetteORCID,Kuzikov Maria,Wolf Markus,Geisslinger Gerd,Gribbon PhilipORCID,Ciesek SandraORCID

Abstract

AbstractSARS-CoV-2 is a novel coronavirus responsible for the COVID-19 pandemic, in which acute respiratory infections are associated with high socio-economic burden. We applied high-content screening to a well-defined collection of 5632 compounds including 3488 that have undergone previous clinical investigations across 600 indications. The compounds were screened by microscopy for their ability to inhibit SARS-CoV-2 cytopathicity in the human epithelial colorectal adenocarcinoma cell line, Caco-2. The primary screen identified 258 hits that inhibited cytopathicity by more than 75%, most of which were not previously known to be active against SARS-CoV-2 in vitro. These compounds were tested in an eight-point dose response screen using the same image-based cytopathicity readout. For the 67 most active molecules, cytotoxicity data were generated to confirm activity against SARS-CoV-2. We verified the ability of known inhibitors camostat, nafamostat, lopinavir, mefloquine, papaverine and cetylpyridinium to reduce the cytopathic effects of SARS-CoV-2, providing confidence in the validity of the assay. The high-content screening data are suitable for reanalysis across numerous drug classes and indications and may yield additional insights into SARS-CoV-2 mechanisms and potential therapeutic strategies.

Funder

LOEWE Center for Translational Medicine and Pharmacology (TMP); Fraunhofer cluster of excellence for immune mediated diseases

LOEWE Center for Translational Medicine and Pharmacology (TMP), Fraunhofer cluster of excellence for immune mediated diseases

EU Horizon 2020 project EOSC-LIFE

Johanna Quandt Universitäts-Stiftung

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability

Reference58 articles.

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