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.
【저자키워드】 Infectious diseases, Respiratory tract diseases, Drug screening, 【초록키워드】 COVID-19, Treatment, coronavirus disease, Transcriptome, SARS-CoV-2, Coronavirus disease 2019, coronavirus, clinical trial, Cytokines, knowledge, SARS-COV-2 infection, machine learning, immunopathology, in silico, severe acute respiratory syndrome Coronavirus, In vitro assay, Spread, Protein, nucleocapsid, Knowledge graph, Inflammatory cytokines, respiratory, inhibitor, SARS-CoV-2 replication, Inflammatory cytokine, Cytopathic effect, statistical analysis, phase, followed by, acute respiratory syndrome, acute respiratory syndrome coronavirus, acute respiratory syndrome coronavirus 2, treat, PARP1, inhibitory activity, drug candidate, polymerase, approach, effective, indicated, addition, suppress, 【제목키워드】 COVID-19, therapeutic,