1区 · 医学
ArticleOA
作者: Yuan, Enming ; Cheng, Lili ; Tian, Tingzhong ; Chi, Ying ; Feng, Xiaolong ; Wang, Xiaoting ; Yang, Chunhao ; Chen, Ligong ; Miao, Zehong ; Li, Haitao ; Jiang, Ziyuan ; Shen, Xiaokun ; Ge, Yiyue ; Wu, Yunfu ; Luo, Yunan ; Wan, Fangping ; Wu, Nian ; Guo, Xiling ; Huang, Suling ; Yang, Hui ; Li, Zeng ; Shu, Hantao ; Zhu, Fengcai ; Zeng, Jianyang ; Xiao, Liang ; Hu, Chengliang ; Cui, Lunbiao ; Li, Shuya ; Li, Jingxin ; Zeng, Hainian ; Lei, Yipin ; Hong, Lixiang ; Zhao, Dan
Abstract:The global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires an urgent need to find effective therapeutics for the treatment of coronavirus disease 2019 (COVID-19). In this study, we developed an integrative drug repositioning framework, which fully takes advantage of machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 can interact with the nucleocapsid (N) protein of SARS-CoV-2 and is able to suppress the LPS-induced production of several inflammatory cytokines that are highly relevant to the prevention of immunopathology induced by SARS-CoV-2 infection.