Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence

Abstract Since the 2019 novel coronavirus disease (COVID-19) outbreak in 2019 and the pandemic continues for more than one year, a vast amount of drug research has been conducted and few of them got FDA approval. Our objective is to prioritize repurposable drugs using a pipeline that systematically...

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Autores principales:	Kanglin Hsieh, Yinyin Wang, Luyao Chen, Zhongming Zhao, Sean Savitz, Xiaoqian Jiang, Jing Tang, Yejin Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/62b2501ae06e46a290cae495d7afa94f
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spelling	oai:doaj.org-article:62b2501ae06e46a290cae495d7afa94f2021-12-05T12:14:14ZDrug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence10.1038/s41598-021-02353-52045-2322https://doaj.org/article/62b2501ae06e46a290cae495d7afa94f2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02353-5https://doaj.org/toc/2045-2322Abstract Since the 2019 novel coronavirus disease (COVID-19) outbreak in 2019 and the pandemic continues for more than one year, a vast amount of drug research has been conducted and few of them got FDA approval. Our objective is to prioritize repurposable drugs using a pipeline that systematically integrates the interaction between COVID-19 and drugs, deep graph neural networks, and in vitro/population-based validations. We first collected all available drugs (n = 3635) related to COVID-19 patient treatment through CTDbase. We built a COVID-19 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate drug’s representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and population-based treatment effect. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and multiple evidence can facilitate the rapid identification of candidate drugs for COVID-19 treatment.Kanglin HsiehYinyin WangLuyao ChenZhongming ZhaoSean SavitzXiaoqian JiangJing TangYejin KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kanglin Hsieh Yinyin Wang Luyao Chen Zhongming Zhao Sean Savitz Xiaoqian Jiang Jing Tang Yejin Kim Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence
description	Abstract Since the 2019 novel coronavirus disease (COVID-19) outbreak in 2019 and the pandemic continues for more than one year, a vast amount of drug research has been conducted and few of them got FDA approval. Our objective is to prioritize repurposable drugs using a pipeline that systematically integrates the interaction between COVID-19 and drugs, deep graph neural networks, and in vitro/population-based validations. We first collected all available drugs (n = 3635) related to COVID-19 patient treatment through CTDbase. We built a COVID-19 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate drug’s representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and population-based treatment effect. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and multiple evidence can facilitate the rapid identification of candidate drugs for COVID-19 treatment.
format	article
author	Kanglin Hsieh Yinyin Wang Luyao Chen Zhongming Zhao Sean Savitz Xiaoqian Jiang Jing Tang Yejin Kim
author_facet	Kanglin Hsieh Yinyin Wang Luyao Chen Zhongming Zhao Sean Savitz Xiaoqian Jiang Jing Tang Yejin Kim
author_sort	Kanglin Hsieh
title	Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence
title_short	Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence
title_full	Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence
title_fullStr	Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence
title_full_unstemmed	Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence
title_sort	drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/62b2501ae06e46a290cae495d7afa94f
work_keys_str_mv	AT kanglinhsieh drugrepurposingforcovid19usinggraphneuralnetworkandharmonizingmultipleevidence AT yinyinwang drugrepurposingforcovid19usinggraphneuralnetworkandharmonizingmultipleevidence AT luyaochen drugrepurposingforcovid19usinggraphneuralnetworkandharmonizingmultipleevidence AT zhongmingzhao drugrepurposingforcovid19usinggraphneuralnetworkandharmonizingmultipleevidence AT seansavitz drugrepurposingforcovid19usinggraphneuralnetworkandharmonizingmultipleevidence AT xiaoqianjiang drugrepurposingforcovid19usinggraphneuralnetworkandharmonizingmultipleevidence AT jingtang drugrepurposingforcovid19usinggraphneuralnetworkandharmonizingmultipleevidence AT yejinkim drugrepurposingforcovid19usinggraphneuralnetworkandharmonizingmultipleevidence
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Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence

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