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Article

Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2

1
Department of Molecular Medicine II, Medical Faculty, Heinrich-Heine-University, 40225 Düsseldorf, Germany
2
Mathematical Modelling of Biological Systems, Heinrich-Heine-University, 40225 Düsseldorf, Germany
3
Institute of Virology, Medical Faculty, Heinrich-Heine-University, 40225 Düsseldorf, Germany
4
Department of Pediatric Oncology, Hematology and Clinical Immunology, Medical Faculty, Center of Child and Adolescent Health, Heinrich-Heine-University, 40225 Düsseldorf, Germany
5
Department of Medicine III, University Hospital RWTH Aachen, 52074 Aachen, Germany
6
Institute of Immunology, Medical Faculty, University of Duisburg-Essen, 45147 Essen, Germany
*
Author to whom correspondence should be addressed.
These authors are equal parts first authors.
Academic Editor: Meehyein Kim
Viruses 2021, 13(4), 610; https://doi.org/10.3390/v13040610
Received: 27 January 2021 / Revised: 25 March 2021 / Accepted: 30 March 2021 / Published: 2 April 2021
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes COVID-19 and is responsible for the ongoing pandemic. Screening of potential antiviral drugs against SARS-CoV-2 depend on in vitro experiments, which are based on the quantification of the virus titer. Here, we used virus-induced cytopathic effects (CPE) in brightfield microscopy of SARS-CoV-2-infected monolayers to quantify the virus titer. Images were classified using deep transfer learning (DTL) that fine-tune the last layers of a pre-trained Resnet18 (ImageNet). To exclude toxic concentrations of potential drugs, the network was expanded to include a toxic score (TOX) that detected cell death (CPETOXnet). With this analytic tool, the inhibitory effects of chloroquine, hydroxychloroquine, remdesivir, and emetine were validated. Taken together we developed a simple method and provided open access implementation to quantify SARS-CoV-2 titers and drug toxicity in experimental settings, which may be adaptable to assays with other viruses. The quantification of virus titers from brightfield images could accelerate the experimental approach for antiviral testing. View Full-Text
Keywords: SARS-CoV-2; deep transfer learning; deep learning; drug screening; emetine; chloroquine; remdesivir; hydroxychloroquine SARS-CoV-2; deep transfer learning; deep learning; drug screening; emetine; chloroquine; remdesivir; hydroxychloroquine
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MDPI and ACS Style

Werner, J.; Kronberg, R.M.; Stachura, P.; Ostermann, P.N.; Müller, L.; Schaal, H.; Bhatia, S.; Kather, J.N.; Borkhardt, A.; Pandyra, A.A.; Lang, K.S.; Lang, P.A. Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2. Viruses 2021, 13, 610. https://doi.org/10.3390/v13040610

AMA Style

Werner J, Kronberg RM, Stachura P, Ostermann PN, Müller L, Schaal H, Bhatia S, Kather JN, Borkhardt A, Pandyra AA, Lang KS, Lang PA. Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2. Viruses. 2021; 13(4):610. https://doi.org/10.3390/v13040610

Chicago/Turabian Style

Werner, Julia, Raphael M. Kronberg, Pawel Stachura, Philipp N. Ostermann, Lisa Müller, Heiner Schaal, Sanil Bhatia, Jakob N. Kather, Arndt Borkhardt, Aleksandra A. Pandyra, Karl S. Lang, and Philipp A. Lang. 2021. "Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2" Viruses 13, no. 4: 610. https://doi.org/10.3390/v13040610

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