In silico drug repurposing in COVID-19: A network-based analysis

Sibilio, Pasquale; Bini, Simone; Fiscon, Giulia; Sponziello, Marialuisa; Conte, Federica; Pecce, Valeria; Durante, Cosimo; Paci, Paola; Falcone, Rosa; Norata, Giuseppe Danilo; Farina, Lorenzo; Verrienti, Antonella

doi:10.1016/j.biopha.2021.111954

Cited by 20 publications

(14 citation statements)

References 83 publications

(105 reference statements)

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“…There are also clinical trials with non-antineoplastic agents as the primary drug being investigated for the treatment of COVID-19 (number of trials in parentheses): simvastatin (3), ethanol (5), curcumin (17), clotrimazole (18), ritonavir (272), glutathione (6), N-acetylcysteine (37), and hydrogen peroxide (20). A subset of these agents and trials are being investigated specifically for moderate to severe or critical COVID-19 patients (number of trials in parentheses): simvastatin (3), ethanol (2), curcumin (5), clotrimazole (8), ritonavir (125), glutathione (7), sirolimus (6), N-acetylcysteine (14) and thalidomide (7); these are shown in Table 7.…”

Section: Drug Enrichment Analysismentioning

confidence: 99%

“…This methodology is based on in silico disease biology modelling which aims to recapitulate the molecular environment of diseased target cells to identify candidate biomarkers and/or drugs that can be proposed as options for disease treatment. Others studying SARS-CoV-2 infection and COVID-19 have used protein-protein interaction (PPI) networks to analyze host-virus protein-protein interaction [8][9][10] , describe the interactome of the coronavirus S-glycoprotein and host proteins 11 , investigate viral-viral and virus-host PPI networks in peripheral blood mononuclear cells 12 , analyze the immune system PPI network and review potential therapeutic targets for drug repurposing against COVID-19 13 , and identify potentially repurposable drugs to treat COVID-19 [14][15][16] .…”

Section: Introductionmentioning

confidence: 99%

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Modeling COVID-19 disease biology to identify drug treatment candidates

Jessen

Gaisenband

Quintanilla

et al. 2022

Preprint

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Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Currently, there are a limited number of effective treatments. A variety of drugs that have been approved for other diseases are being tested for the treatment of COVID-19, and thus far only remdesevir, dexamethasone, baricitinib, tofacitinib, tocilizumab, and sarilumab have been recommended by the National Institutes of Health (NIH) COVID-19 Treatment Guidelines Panel for the therapeutic management of hospitalized adults with COVID-19. Using a disease biology modeling approach, we constructed a protein-protein interactome network based on COVID-19- associated genes/proteins described in research literature together with known protein-protein interactions in epithelial cells. Phenotype and disease enrichment analysis of the COVID-19 disease biology model demonstrated strong statistical enrichments consistent with patients’ clinical presentation. The model was used to interrogate host biological response induced by SARS-CoV-2 and identify COVID-19 drug treatment candidates that may inform on drugs currently being evaluated or provide insight into possible targets for potential new therapeutic agents. We focused on cancer drugs as they are often used to control inflammation, inhibit cell division, and modulate the host microenvironment to control the disease. From the top 30 COVID-19 drug candidates, twelve have a role as an antineoplastic agent, seven of which are approved for human use. Altogether, nearly 40% of the drugs identified by our model have been identified by others for COVID-19 clinical trials. Disease biology modeling incorporating disease-associated genes/proteins discussed in the research literature together with known molecular interactions in relevant cell types is a useful method to better understand disease biology and identify potentially effective therapeutic interventions.

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Section: Drug Enrichment Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling COVID-19 disease biology to identify drug treatment candidates

Jessen

Gaisenband

Quintanilla

et al. 2022

Preprint

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“…On august 15,2019, FDA gave approval for Entrectinib drug for the treatment of solid tumors that have NTRK gene fusion without a known acquired resistance mutation are metastatic or result in severe morbidity if surgical resection is performed and have no alternative therapy or have progressed following treatment is the current status of Entrectinib [43]. Currently drug repositioning works are also performed in Entrectinib for the estimation of anti-SARS-CoV-2 activity of the drug [44]. Current and future research may assist to define the ideal place for Entrectinib treatment.…”

Section: Current Status and Future Of Entrectinibmentioning

confidence: 99%

An updated Review on Therapeutic Potential of Entrectinib as a Promising TrK, ROS 1, and ALK Inhibitor in Solid Tumors and Lung Cancer

Archana¹,

Palathingal²,

Kannan³

et al. 2021

JPRI

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Entrectinib is a selective inhibitor of tyrosine kinases , tropomyosin receptor kinases that targets oncogenic rearrangements in Neurotropic Tyrosine Receptor kinase, c-ros oncogene 1 and Anaplastic lymphoma kinase used for the treatment of various solid tumors. Entrectinib gained its first worldwide approval in Japan in June 2019 for the treatment of NTRK fusion-positive, advanced or recurring solid tumours in adults and children. In august 15, 2019 drug got FDA approval for the treatment of solid tumors in adult and children aged 12 and above. This article summarizes current status of Entrectinib from ongoing clinical trails and ideal place for drug in therapy.

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“…Although many kinds of vaccines have been developed for prevention, there is a huge demand to find therapeutic agents as soon as possible [4,5] . Among various approaches, computational drug repurposing has shown a great advantage compared to others [6–11] . Remdesvir [12] is an repurposing example that was approved for the treatment of COVID‐19 by U.S. FDA on Oct. 22, 2020.…”

Section: Introductionmentioning

confidence: 99%

“…[4,5] Among various approaches, computational drug repurposing has shown a great advantage compared to others. [6][7][8][9][10][11] Remdesvir [12] is an repurposing example that was approved for the treatment of COVID-19 by U.S. FDA on Oct. 22, 2020. Therefore, it is urgent to develop new computational drug repurposing method for such a newly emerged disease.…”

Section: Introductionmentioning

confidence: 99%

Drug Repurposing for Newly Emerged Diseases via Network‐based Inference on a Gene‐disease‐drug Network

Qin

Wang

et al. 2022

Molecular Informatics

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Identification of disease-drug associations is an effective strategy for drug repurposing, especially in searching old drugs for newly emerged diseases like COVID-19. In this study, we put forward a network-based method named NEDNBI to predict disease-drug associations based on a gene-disease-drug tripartite network, which could be applied in drug repurposing. The novelty of our method lies in the fact that no negative data are required, and new disease could be added into the disease-drug network with gene as the bridge. The comprehensive evaluation results showed that the proposed method had good performance, with AUC value 0.948 � 0.009 for 10-fold cross validation. In a case study, 8 of the 20 predicted old drugs have been tested clinically for the treatment of COVID-19, which illustrated the usefulness of our method in drug repurposing. The source code and data of the method are available at https://github.com/Qli97/NEDNBI.

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In silico drug repurposing in COVID-19: A network-based analysis

Cited by 20 publications

References 83 publications

Modeling COVID-19 disease biology to identify drug treatment candidates

Modeling COVID-19 disease biology to identify drug treatment candidates

An updated Review on Therapeutic Potential of Entrectinib as a Promising TrK, ROS 1, and ALK Inhibitor in Solid Tumors and Lung Cancer

Drug Repurposing for Newly Emerged Diseases via Network‐based Inference on a Gene‐disease‐drug Network

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