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1000 Titel
  • SAveRUNNER: A network-based algorithm for drug repurposing and its application to COVID-19
1000 Autor/in
  1. FISCON, GIULIA |
  2. Conte, Federica |
  3. Farina, Lorenzo |
  4. Paci, Paola |
1000 Erscheinungsjahr 2021
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2021-02-05
1000 Erschienen in
1000 Quellenangabe
  • 17(2):e1008686
1000 Copyrightjahr
  • 2021
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1371/journal.pcbi.1008686 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7891752 |
1000 Ergänzendes Material
  • https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008686#sec032 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we presented a new network-based algorithm for drug repositioning, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug–disease associations by quantifying the interplay between the drug targets and the disease-specific proteins in the human interactome via a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Specifically, we applied SAveRUNNER on a panel of 14 selected diseases with a consolidated knowledge about their disease-causing genes and that have been found to be related to COVID-19 for genetic similarity (i.e., SARS), comorbidity (e.g., cardiovascular diseases), or for their association to drugs tentatively repurposed to treat COVID-19 (e.g., malaria, HIV, rheumatoid arthritis). Focusing specifically on SARS subnetwork, we identified 282 repurposable drugs, including some the most rumored off-label drugs for COVID-19 treatments (e.g., chloroquine, hydroxychloroquine, tocilizumab, heparin), as well as a new combination therapy of 5 drugs (hydroxychloroquine, chloroquine, lopinavir, ritonavir, remdesivir), actually used in clinical practice. Furthermore, to maximize the efficiency of putative downstream validation experiments, we prioritized 24 potential anti-SARS-CoV repurposable drugs based on their network-based similarity values. These top-ranked drugs include ACE-inhibitors, monoclonal antibodies (e.g., anti-IFNγ, anti-TNFα, anti-IL12, anti-IL1β, anti-IL6), and thrombin inhibitors. Finally, our findings were in-silico validated by performing a gene set enrichment analysis, which confirmed that most of the network-predicted repurposable drugs may have a potential treatment effect against human coronavirus infections.
1000 Sacherschließung
gnd 1206347392 COVID-19
lokal SARS CoV 2
lokal Drug therapy
lokal Drug discovery
lokal Genetic networks
lokal SARS
lokal SARS coronavirus
lokal Interaction networks
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0002-3354-8203|https://orcid.org/0000-0003-0427-1476|https://frl.publisso.de/adhoc/uri/RmFyaW5hLCBMb3Jlbnpv|https://orcid.org/0000-0002-9393-2047
1000 Label
1000 Förderer
  1. Consiglio Nazionale delle Ricerche |
  2. Sapienza Università di Roma |
1000 Fördernummer
  1. 20178L3P38
  2. RM1181642AFA34C2
1000 Förderprogramm
  1. PRIN 2017 - Settore ERC LS2
  2. Network medicine-based machine learning and graph theory algorithms for precision oncology
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Consiglio Nazionale delle Ricerche |
    1000 Förderprogramm PRIN 2017 - Settore ERC LS2
    1000 Fördernummer 20178L3P38
  2. 1000 joinedFunding-child
    1000 Förderer Sapienza Università di Roma |
    1000 Förderprogramm Network medicine-based machine learning and graph theory algorithms for precision oncology
    1000 Fördernummer RM1181642AFA34C2
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6428021.rdf
1000 Erstellt am 2021-06-08T10:44:46.864+0200
1000 Erstellt von 284
1000 beschreibt frl:6428021
1000 Bearbeitet von 25
1000 Zuletzt bearbeitet Tue Nov 09 10:36:21 CET 2021
1000 Objekt bearb. Tue Nov 09 10:36:03 CET 2021
1000 Vgl. frl:6428021
1000 Oai Id
  1. oai:frl.publisso.de:frl:6428021 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

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