An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19

Ge, Yiyue; Tian, Tingzhong; Huang, Suling; Wan, Fangping; Li, Jingxin; Li, Shuya; Wang, Xiaoting; Yang, Hui; Hong, Lixiang; Wu, Nian; Yuan, Enming; Luo, Yunan; Cheng, Lili; Hu, Chengliang; Lei, Yipin; Shu, Hantao; Feng, Xiaolong; Jiang, Ziyuan; Wu, Yunfu; Chi, Ying; Guo, Xiling; Cui, Lunbiao; Xiao, Liang; Li, Zeng; Yang, Chunhao; Miao, Zehong; Chen, Ligong; Li, Haitao; Zeng, Hainian; Zhao, Dan; Zhu, Fengcai; Shen, Xiaokun; Zeng, Jianyang

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.