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Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2
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 cy...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8066066/ https://www.ncbi.nlm.nih.gov/pubmed/33918368 http://dx.doi.org/10.3390/v13040610 |
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author | Werner, Julia Kronberg, Raphael M. Stachura, Pawel Ostermann, Philipp N. Müller, Lisa Schaal, Heiner Bhatia, Sanil Kather, Jakob N. Borkhardt, Arndt Pandyra, Aleksandra A. Lang, Karl S. Lang, Philipp A. |
author_facet | Werner, Julia Kronberg, Raphael M. Stachura, Pawel Ostermann, Philipp N. Müller, Lisa Schaal, Heiner Bhatia, Sanil Kather, Jakob N. Borkhardt, Arndt Pandyra, Aleksandra A. Lang, Karl S. Lang, Philipp A. |
author_sort | Werner, Julia |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8066066 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80660662021-04-25 Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2 Werner, Julia Kronberg, Raphael M. Stachura, Pawel Ostermann, Philipp N. Müller, Lisa Schaal, Heiner Bhatia, Sanil Kather, Jakob N. Borkhardt, Arndt Pandyra, Aleksandra A. Lang, Karl S. Lang, Philipp A. Viruses Article 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. MDPI 2021-04-02 /pmc/articles/PMC8066066/ /pubmed/33918368 http://dx.doi.org/10.3390/v13040610 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Werner, Julia Kronberg, Raphael M. Stachura, Pawel Ostermann, Philipp N. Müller, Lisa Schaal, Heiner Bhatia, Sanil Kather, Jakob N. Borkhardt, Arndt Pandyra, Aleksandra A. Lang, Karl S. Lang, Philipp A. Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2 |
title | Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2 |
title_full | Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2 |
title_fullStr | Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2 |
title_full_unstemmed | Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2 |
title_short | Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2 |
title_sort | deep transfer learning approach for automatic recognition of drug toxicity and inhibition of sars-cov-2 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8066066/ https://www.ncbi.nlm.nih.gov/pubmed/33918368 http://dx.doi.org/10.3390/v13040610 |
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