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Drug repositioning by merging active subnetworks validated in cancer and COVID-19

Computational drug repositioning aims at ranking and selecting existing drugs for novel diseases or novel use in old diseases. In silico drug screening has the potential for speeding up considerably the shortlisting of promising candidates in response to outbreaks of diseases such as COVID-19 for wh...

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Autores principales: Lucchetta, Marta, Pellegrini, Marco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8494853/
https://www.ncbi.nlm.nih.gov/pubmed/34615934
http://dx.doi.org/10.1038/s41598-021-99399-2
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author Lucchetta, Marta
Pellegrini, Marco
author_facet Lucchetta, Marta
Pellegrini, Marco
author_sort Lucchetta, Marta
collection PubMed
description Computational drug repositioning aims at ranking and selecting existing drugs for novel diseases or novel use in old diseases. In silico drug screening has the potential for speeding up considerably the shortlisting of promising candidates in response to outbreaks of diseases such as COVID-19 for which no satisfactory cure has yet been found. We describe DrugMerge as a methodology for preclinical computational drug repositioning based on merging multiple drug rankings obtained with an ensemble of disease active subnetworks. DrugMerge uses differential transcriptomic data on drugs and diseases in the context of a large gene co-expression network. Experiments with four benchmark diseases demonstrate that our method detects in first position drugs in clinical use for the specified disease, in all four cases. Application of DrugMerge to COVID-19 found rankings with many drugs currently in clinical trials for COVID-19 in top positions, thus showing that DrugMerge can mimic human expert judgment.
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spelling pubmed-84948532021-10-08 Drug repositioning by merging active subnetworks validated in cancer and COVID-19 Lucchetta, Marta Pellegrini, Marco Sci Rep Article Computational drug repositioning aims at ranking and selecting existing drugs for novel diseases or novel use in old diseases. In silico drug screening has the potential for speeding up considerably the shortlisting of promising candidates in response to outbreaks of diseases such as COVID-19 for which no satisfactory cure has yet been found. We describe DrugMerge as a methodology for preclinical computational drug repositioning based on merging multiple drug rankings obtained with an ensemble of disease active subnetworks. DrugMerge uses differential transcriptomic data on drugs and diseases in the context of a large gene co-expression network. Experiments with four benchmark diseases demonstrate that our method detects in first position drugs in clinical use for the specified disease, in all four cases. Application of DrugMerge to COVID-19 found rankings with many drugs currently in clinical trials for COVID-19 in top positions, thus showing that DrugMerge can mimic human expert judgment. Nature Publishing Group UK 2021-10-06 /pmc/articles/PMC8494853/ /pubmed/34615934 http://dx.doi.org/10.1038/s41598-021-99399-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Lucchetta, Marta
Pellegrini, Marco
Drug repositioning by merging active subnetworks validated in cancer and COVID-19
title Drug repositioning by merging active subnetworks validated in cancer and COVID-19
title_full Drug repositioning by merging active subnetworks validated in cancer and COVID-19
title_fullStr Drug repositioning by merging active subnetworks validated in cancer and COVID-19
title_full_unstemmed Drug repositioning by merging active subnetworks validated in cancer and COVID-19
title_short Drug repositioning by merging active subnetworks validated in cancer and COVID-19
title_sort drug repositioning by merging active subnetworks validated in cancer and covid-19
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8494853/
https://www.ncbi.nlm.nih.gov/pubmed/34615934
http://dx.doi.org/10.1038/s41598-021-99399-2
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