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A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19

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

Posted 12 Mar 2020
bioRxiv DOI: 10.1101/2020.03.11.986836

The global spread of SARS-CoV-2 requires an urgent need to find effective therapeutics for the treatment of COVID-19. We developed a data-driven drug repositioning framework, which applies both 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. The retrospective study using the past SARS-CoV and MERS-CoV data demonstrated that our machine learning based method can successfully predict effective drug candidates against a specific coronavirus. 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 is able to suppress the CpG-induced IL-6 production in peripheral blood mononuclear cells, suggesting that it may also have anti-inflammatory effect that is highly relevant to the prevention immunopathology induced by SARS-CoV-2 infection. Further pharmacokinetic and toxicokinetic evaluation in rats and monkeys showed a high concentration of CVL218 in lung and observed no apparent signs of toxicity, indicating the appealing potential of this drug for the treatment of the pneumonia caused by SARS-CoV-2 infection. Moreover, molecular docking simulation suggested that CVL218 may bind to the N-terminal domain of nucleocapsid (N) protein of SARS-CoV-2, providing a possible model to explain its antiviral action. We also proposed several possible mechanisms to explain the antiviral activities of PARP1 inhibitors against SARS-CoV-2, based on the data present in this study and previous evidences reported in the literature. In summary, the PARP1 inhibitor CVL218 discovered by our data-driven drug repositioning framework can serve as a potential therapeutic agent for the treatment of COVID-19.

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