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A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19
Yiyue Ge; Tingzhong Tian; Sulin 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.
Afiliación
  • Yiyue Ge; NHC Key laboratory of Enteric Pathogenic Microbiology, Jiangsu Provincial Center for Diseases Control and Prevention, Nanjing, Jiangsu Province, 210009, China.
  • Tingzhong Tian; Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China.
  • Sulin Huang; Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
  • Fangping Wan; Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China.
  • Jingxin Li; NHC Key laboratory of Enteric Pathogenic Microbiology, Jiangsu Provincial Center for Diseases Control and Prevention, Nanjing, Jiangsu Province, 210009, China.
  • Shuya Li; Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China.
  • Hui Yang; Silexon AI Technology Co., Ltd., Nanjing, Jiangsu Province, 210033, China.
  • Lixiang Hong; Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China.
  • Nian Wu; Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China.
  • Enming Yuan; Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China.
  • Lili Cheng; School of Pharmaceutical Sciences, Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing, 100084, China.
  • Yipin Lei; Silexon AI Technology Co., Ltd., Nanjing, Jiangsu Province, 210033, China.
  • Hantao Shu; Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China.
  • Xiaolong Feng; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430074, China.
  • Ziyuan Jiang; Department of Automation, Tsinghua University, Beijing, 100084, China.
  • Ying Chi; NHC Key laboratory of Enteric Pathogenic Microbiology, Jiangsu Provincial Center for Diseases Control and Prevention, Nanjing, Jiangsu Province, 210009, China.
  • Xiling Guo; NHC Key laboratory of Enteric Pathogenic Microbiology, Jiangsu Provincial Center for Diseases Control and Prevention, Nanjing, Jiangsu Province, 210009, China.
  • Lunbiao Cui; NHC Key laboratory of Enteric Pathogenic Microbiology, Jiangsu Provincial Center for Diseases Control and Prevention, Nanjing, Jiangsu Province, 210009, China.
  • Liang Xiao; Convalife (Shanghai) Co., Ltd., Shanghai, 201203, China.
  • Zeng Li; Convalife (Shanghai) Co., Ltd., Shanghai, 201203, China.
  • Chunhao Yang; Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
  • Zehong Miao; Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
  • Haidong Tang; School of Pharmaceutical Sciences, Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing, 100084, China.
  • Ligong Chen; School of Pharmaceutical Sciences, Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing, 100084, China.
  • Hainian Zeng; Silexon AI Technology Co., Ltd., Nanjing, Jiangsu Province, 210033, China.
  • Dan Zhao; Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China.
  • Fengcai Zhu; NHC Key laboratory of Enteric Pathogenic Microbiology, Jiangsu Provincial Center for Diseases Control and Prevention, Nanjing, Jiangsu Province, 210009, China.
  • Xiaokun Shen; Convalife (Shanghai) Co., Ltd., Shanghai, 201203, China.
  • Jianyang Zeng; Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China.
Preprint en En | PREPRINT-BIORXIV | ID: ppbiorxiv-986836
ABSTRACT
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.
Licencia
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Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-BIORXIV Tipo de estudio: Experimental_studies / Observational_studies / Prognostic_studies / Rct Idioma: En Año: 2020 Tipo del documento: Preprint
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-BIORXIV Tipo de estudio: Experimental_studies / Observational_studies / Prognostic_studies / Rct Idioma: En Año: 2020 Tipo del documento: Preprint