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A network-biology led computational drug repurposing strategy to prioritize therapeutic options for COVID-19

The alarming pandemic situation of novel Severe Acute Respiratory Syndrome Coronavirus 2 (nSARS-CoV-2) infection, high drug development cost and slow process of drug discovery have made repositioning of existing drugs for therapeutics a popular alternative. It involves the repurposing of existing sa...

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Autores principales: Khurana, Pankaj, Varshney, Rajeev, Gupta, Apoorv
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9093055/
https://www.ncbi.nlm.nih.gov/pubmed/35578630
http://dx.doi.org/10.1016/j.heliyon.2022.e09387
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author Khurana, Pankaj
Varshney, Rajeev
Gupta, Apoorv
author_facet Khurana, Pankaj
Varshney, Rajeev
Gupta, Apoorv
author_sort Khurana, Pankaj
collection PubMed
description The alarming pandemic situation of novel Severe Acute Respiratory Syndrome Coronavirus 2 (nSARS-CoV-2) infection, high drug development cost and slow process of drug discovery have made repositioning of existing drugs for therapeutics a popular alternative. It involves the repurposing of existing safe compounds which results in low overall development costs and shorter development timeline. In the present study, a computational network-biology approach has been used for comparing three candidate drugs i.e. quercetin, N-acetyl cysteine (NAC), and 2-deoxy-glucose (2-DG) to be effectively repurposed against COVID-19. For this, the associations between these drugs and genes of Severe Acute Respiratory Syndrome (SARS) and the Middle East Respiratory Syndrome (MERS) diseases were retrieved and a directed drug-gene-gene-disease interaction network was constructed. Further, to quantify the associations between a target gene and a disease gene, the shortest paths from the target gene to the disease genes were identified. A vector DV was calculated to represent the extent to which a disease gene was influenced by these drugs. Quercetin was quantified as the best among the three drugs, suited for repurposing with DV of -70.19, followed by NAC with DV of -39.99 and 2-DG with DV of -13.71. The drugs were also assessed for their safety and efficacy balance (in terms of therapeutic index) using network properties. It was found that quercetin was a forerunner than other two drugs.
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spelling pubmed-90930552022-05-12 A network-biology led computational drug repurposing strategy to prioritize therapeutic options for COVID-19 Khurana, Pankaj Varshney, Rajeev Gupta, Apoorv Heliyon Research Article The alarming pandemic situation of novel Severe Acute Respiratory Syndrome Coronavirus 2 (nSARS-CoV-2) infection, high drug development cost and slow process of drug discovery have made repositioning of existing drugs for therapeutics a popular alternative. It involves the repurposing of existing safe compounds which results in low overall development costs and shorter development timeline. In the present study, a computational network-biology approach has been used for comparing three candidate drugs i.e. quercetin, N-acetyl cysteine (NAC), and 2-deoxy-glucose (2-DG) to be effectively repurposed against COVID-19. For this, the associations between these drugs and genes of Severe Acute Respiratory Syndrome (SARS) and the Middle East Respiratory Syndrome (MERS) diseases were retrieved and a directed drug-gene-gene-disease interaction network was constructed. Further, to quantify the associations between a target gene and a disease gene, the shortest paths from the target gene to the disease genes were identified. A vector DV was calculated to represent the extent to which a disease gene was influenced by these drugs. Quercetin was quantified as the best among the three drugs, suited for repurposing with DV of -70.19, followed by NAC with DV of -39.99 and 2-DG with DV of -13.71. The drugs were also assessed for their safety and efficacy balance (in terms of therapeutic index) using network properties. It was found that quercetin was a forerunner than other two drugs. Elsevier 2022-05-11 /pmc/articles/PMC9093055/ /pubmed/35578630 http://dx.doi.org/10.1016/j.heliyon.2022.e09387 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Khurana, Pankaj
Varshney, Rajeev
Gupta, Apoorv
A network-biology led computational drug repurposing strategy to prioritize therapeutic options for COVID-19
title A network-biology led computational drug repurposing strategy to prioritize therapeutic options for COVID-19
title_full A network-biology led computational drug repurposing strategy to prioritize therapeutic options for COVID-19
title_fullStr A network-biology led computational drug repurposing strategy to prioritize therapeutic options for COVID-19
title_full_unstemmed A network-biology led computational drug repurposing strategy to prioritize therapeutic options for COVID-19
title_short A network-biology led computational drug repurposing strategy to prioritize therapeutic options for COVID-19
title_sort network-biology led computational drug repurposing strategy to prioritize therapeutic options for covid-19
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9093055/
https://www.ncbi.nlm.nih.gov/pubmed/35578630
http://dx.doi.org/10.1016/j.heliyon.2022.e09387
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