scholarly journals An integrated drug repurposing strategy for the rapid identification of potential SARS-CoV-2 viral inhibitors

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alfonso Trezza ◽  
Daniele Iovinelli ◽  
Annalisa Santucci ◽  
Filippo Prischi ◽  
Ottavia Spiga
Keyword(s):  
Drug Repurposing ◽  
Rapid Identification ◽  
Viral Inhibitors

scholarly journals Screening a library of FDA-approved and bioactive compounds for antiviral activity against SARS-CoV-2

2020 ◽  
Author(s):  
Scott B. Biering ◽  
Erik Van Dis ◽  
Eddie Wehri ◽  
Livia H. Yamashiro ◽  
Xammy Nguyenla ◽  
...  
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Combination Therapy ◽  
Global Health ◽  
Antiviral Activity ◽  
Drug Repurposing ◽  
Rapid Identification ◽  
Screening Strategy ◽  
Combination Therapies ◽  
Pulmonary Epithelial Cells ◽  
Compound Screening

AbstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has emerged as a major global health threat. The COVID-19 pandemic has resulted in over 80 million cases and 1.7 million deaths to date while the number of cases continues to rise. With limited therapeutic options, the identification of safe and effective therapeutics is urgently needed. The repurposing of known clinical compounds holds the potential for rapid identification of drugs effective against SARS-CoV-2. Here we utilized a library of FDA-approved and well-studied preclinical and clinical compounds to screen for antivirals against SARS-CoV-2 in human pulmonary epithelial cells. We identified 13 compounds that exhibit potent antiviral activity across multiple orthogonal assays. Hits include known antivirals, compounds with anti-inflammatory activity, and compounds targeting host pathways such as kinases and proteases critical for SARS-CoV-2 replication. We identified seven compounds not previously reported to have activity against SARS-CoV-2, including B02, a human RAD51 inhibitor. We further demonstrated that B02 exhibits synergy with remdesivir, the only antiviral approved by the FDA to treat COVID-19, highlighting the potential for combination therapy. Taken together, our comparative compound screening strategy highlights the potential of drug repurposing screens to identify novel starting points for development of effective antiviral mono- or combination therapies to treat COVID-19.

scholarly journals Rapid Identification of Antifungal Compounds against Exserohilum rostratum Using High Throughput Drug Repurposing Screens

PLoS ONE ◽  
2013 ◽  
Vol 8 (8) ◽  
pp. e70506 ◽  
Author(s):  
Wei Sun ◽  
Yoon-Dong Park ◽  
Janyce A. Sugui ◽  
Annette Fothergill ◽  
Noel Southall ◽  
...  
Keyword(s):  
High Throughput ◽  
Drug Repurposing ◽  
Rapid Identification ◽  
Antifungal Compounds ◽  
Exserohilum Rostratum

scholarly journals Functional immune mapping with deep-learning enabled phenomics applied to immunomodulatory and COVID-19 drug discovery

2020 ◽  
Author(s):  
Michael F. Cuccarese ◽  
Berton A. Earnshaw ◽  
Katie Heiser ◽  
Ben Fogelson ◽  
Chadwick T. Davis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
High Throughput Screening ◽  
Drug Repurposing ◽  
Rapid Identification ◽  
Large Molecules ◽  
Tnf Α ◽  
Genetic Perturbations ◽  
Compound Screening ◽  
Model Drug

ABSTRACTDevelopment of accurate disease models and discovery of immune-modulating drugs is challenged by the immune system’s highly interconnected and context-dependent nature. Here we apply deep-learning-driven analysis of cellular morphology to develop a scalable “phenomics” platform and demonstrate its ability to identify dose-dependent, high-dimensional relationships among and between immunomodulators, toxins, pathogens, genetic perturbations, and small and large molecules at scale. High-throughput screening on this platform demonstrates rapid identification and triage of hits for TGF-β- and TNF-α-driven phenotypes. We deploy the platform to develop phenotypic models of active SARS-CoV-2 infection and of COVID-19-associated cytokine storm, surfacing compounds with demonstrated clinical benefit and identifying several new candidates for drug repurposing. The presented library of images, deep learning features, and compound screening data from immune profiling and COVID-19 screens serves as a deep resource for immune biology and cellular-model drug discovery with immediate impact on the COVID-19 pandemic.

scholarly journals An integrated drug repurposing strategy for the rapid identification of potential SARS-CoV-2 viral inhibitors

2020 ◽  
Author(s):  
Alfonso Trezza ◽  
Daniele Iovinelli ◽  
Filippo Prischi ◽  
Annalisa Santucci ◽  
Ottavia Spiga
Keyword(s):  
Host Cell ◽  
Viral Entry ◽  
In Silico ◽  
De Novo ◽  
Drug Repurposing ◽  
Rapid Identification ◽  
Spike Protein ◽  
Effective Vaccine ◽  
Docking Simulations ◽  
Approved Drugs

Abstract The Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2). The virus has rapidly spread in humans, causing the ongoing Coronavirus pandemic. Recent studies have shown that, similarly to SARS-CoV, SARS-CoV-2 utilises the Spike glycoprotein on the envelope to recognise and bind the human receptor ACE2. This event initiates the fusion of viral and host cell membranes and then the viral entry into the host cell. Despite several ongoing clinical studies, there are currently no approved vaccines or drugs that specifically target SARS-CoV-2. Until an effective vaccine is available, repurposing FDA approved drugs could significantly shorten the time and reduce the cost compared to de novo drug discovery. In this study we attempted to overcome the limitation of in silico virtual screening by applying a robust in silico drug repurposing strategy. We combined and integrated docking simulations, with molecular dynamics (MD), Supervised MD (SuMD) and Steered MD (SMD) simulations to identify a Spike protein – ACE2 interaction inhibitor. Our data showed that Nilotinib and Imatinib bind the receptor-binding domain of the Spike protein with high affinity and prevent ACE2 interaction.

scholarly journals Rapid Identification of Druggable Targets and the Power of the PHENotype SIMulator for Effective Drug Repurposing in COVID-19

2021 ◽  
Author(s):  
Naomi MARIA ◽  
Rosaria Valentina Rapicavoli ◽  
Salvatore Alaimo ◽  
Evelyne Bischof ◽  
Alessia Stasuzzo ◽  
...  
Keyword(s):  
Immune Response ◽  
Drug Repurposing ◽  
High Sensitivity ◽  
Host Immune Response ◽  
Rapid Identification ◽  
Therapeutic Strategies ◽  
Host Cells ◽  
Drug Candidates ◽  
Domain Expertise ◽  
Druggable Targets

Abstract The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with very few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which ̶ by leveraging available transcriptomic and proteomic databases ̶ allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both > 96%) the viral effects on cellular host-immune response, resulting in a specific cellular SARS-CoV-2 signature and ii) utilize this specific signature to narrow down promising repurposable therapeutic strategies. Powered by this tool, coupled with domain expertise, we have identified several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential new druggable targets in COVID-19 pathogenesis.

scholarly journals An integrated drug repurposing strategy for the rapid identification of potential SARS-CoV-2 viral inhibitors

2020 ◽  
Author(s):  
Alfonso Trezza ◽  
Daniele Iovinelli ◽  
Filippo Prischi ◽  
Annalisa Santucci ◽  
Ottavia Spiga
Keyword(s):  
Host Cell ◽  
Viral Entry ◽  
In Silico ◽  
De Novo ◽  
Drug Repurposing ◽  
Rapid Identification ◽  
Spike Protein ◽  
Effective Vaccine ◽  
Docking Simulations ◽  
Approved Drugs

Abstract The Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2). The virus has rapidly spread in humans, causing the ongoing Coronavirus pandemic. Recent studies have shown that, similarly to SARS-CoV, SARS-CoV-2 utilises the Spike glycoprotein on the envelope to recognise and bind the human receptor ACE2. This event initiates the fusion of viral and host cell membranes and then the viral entry into the host cell. Despite several ongoing clinical studies, there are currently no approved vaccines or drugs that specifically target SARS-CoV-2. Until an effective vaccine is available, repurposing FDA approved drugs could significantly shorten the time and reduce the cost compared to de novo drug discovery. In this study we attempted to overcome the limitation of in silico virtual screening by applying a robust in silico drug repurposing strategy. We combined and integrated docking simulations, with molecular dynamics (MD), Supervised MD (SuMD) and Steered MD (SMD) simulations to identify a Spike protein – ACE2 interaction inhibitor. Our data showed that Simeprevir and Lumacaftor bind the receptor-binding domain of the Spike protein with high affinity and prevent ACE2 interaction.Authors Alfonso Trezza and Daniele Iovinelli contributed equally to this work.

scholarly journals A Graph Convolutional Network-based screening strategy for rapid identification of SARS-CoV-2 cell-entry inhibitors

2021 ◽  
Author(s):  
Peng Gao ◽  
Miao Xu ◽  
Qi Zhang ◽  
Catherine Chen ◽  
Hui Guo ◽  
...  
Keyword(s):  
Drug Targets ◽  
Drug Repurposing ◽  
Rapid Identification ◽  
Screening Strategy ◽  
Cell Entry ◽  
Classification Model ◽  
Convolutional Network ◽  
Entry Inhibitors ◽  
Drug Identification ◽  
Conventional Drug

The cell entry of SARS-CoV-2 has emerged as an attractive drug development target. We previously reported that the entry of SARS-CoV-2 depends on the cell surface heparan sulfate proteoglycan (HSPG) and the cortex actin, which can be targeted by therapeutic agents identified by conventional drug repurposing screens. However, this drug identification strategy requires laborious library screening, which is time-consuming and often limited number of compounds can be screened. As an alternative approach, we developed and trained a graph convolutional network (GCN)-based classification model using information extracted from experimentally identified HSPG and actin inhibitors. This method allowed us to virtually screen 170,000 compounds, resulting in ~2000 potential hits. A hit confirmation assay with the uptake of a fluorescently labeled HSPG cargo further shortlisted 256 active compounds. Among them, 16 compounds had modest to strong inhibitory activities against the entry of SARS-CoV-2 pseudotyped particles into Vero E6 cells. These results establish a GCN-based virtual screen workflow for rapid identification of new small molecule inhibitors against validated drug targets.

scholarly journals Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kanglin Hsieh ◽  
Yinyin Wang ◽  
Luyao Chen ◽  
Zhongming Zhao ◽  
Sean Savitz ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Drug Repurposing ◽  
Population Based ◽  
Rapid Identification ◽  
Patient Treatment ◽  
Biological Interactions ◽  
Neural Network Approach ◽  
Trial History

AbstractSince the 2019 novel coronavirus disease (COVID-19) outbreak in 2019 and the pandemic continues for more than one year, a vast amount of drug research has been conducted and few of them got FDA approval. Our objective is to prioritize repurposable drugs using a pipeline that systematically integrates the interaction between COVID-19 and drugs, deep graph neural networks, and in vitro/population-based validations. We first collected all available drugs (n = 3635) related to COVID-19 patient treatment through CTDbase. We built a COVID-19 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate drug’s representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and population-based treatment effect. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and multiple evidence can facilitate the rapid identification of candidate drugs for COVID-19 treatment.

scholarly journals Drug Repurposing for COVID-19 using Graph Neural Network with Genetic, Mechanistic, and Epidemiological Validation

2020 ◽  
Author(s):  
Kang-Lin Hsieh ◽  
Yinyin Wang ◽  
Luyao Chen ◽  
Zhongming Zhao ◽  
Sean Savitz ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Drug Repurposing ◽  
Population Based ◽  
Rapid Identification ◽  
Patient Treatment ◽  
Biological Interactions ◽  
Neural Network Approach ◽  
Trial History

Abstract Amid the pandemic of 2019 novel coronavirus disease (COVID-19) infected by SARS-CoV-2, a vast amount of drug research for prevention and treatment has been quickly conducted, but these efforts have been unsuccessful thus far. Our objective is to prioritize repurposable drugs using a drug repurposing pipeline that systematically integrates multiple SARS-CoV-2 and drug interactions, deep graph neural networks, and in-vitro/population-based validations. We first collected all the available drugs (n= 3,635) involved in COVID-19 patient treatment through CTDbase. We built a SARS-CoV-2 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate drug’s representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and electronic health records. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment. This paper had been uploaded to arXiv : https://arxiv.org/abs/2009.10931

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