Evidence map›Paper›PMID 33154404›Full record

ArticleScientific reports2020

Searching for target-specific and multi-targeting organics for Covid-19 in the Drugbank database with a double scoring approach.

Natarajan Arul Murugan, Sanjiv Kumar, Jeyaraman Jeyakanthan, Vaibhav Srivastava

Abstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Exploring the Therapeutic Potential ofMolecules (Basel, Switzerland) · 2024
    Article
  7. Article
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  10. Effect of farnesyltransferase inhibitors on SARS-CoV-2.Journal of global antimicrobial resistance · 2023
    Article
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  12. Article
  13. Review
  14. Article
  15. Rifampicin for COVID-19.World journal of virology · 2022
    Review
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  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Natarajan Arul MuruganDepartment of Theoretical Chemistry and Biology, School of Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, 106 91, Stockholm, Sweden. murugan@kth.se.
Sanjiv KumarDivision of Glycoscience, Department of Chemistry, School of Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.
Jeyaraman JeyakanthanDepartment of Bioinformatics, Alagappa University, Karaikudi, Tamilnadu, India.
Vaibhav SrivastavaDivision of Glycoscience, Department of Chemistry, School of Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden. vasri@kth.se.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The current outbreak of Covid-19 infection due to SARS-CoV-2, a virus from the coronavirus family, has become a major threat to human healthcare. The virus has already infected more than 44 M people and the number of deaths reported has reached more than 1.1 M which may be attributed to lack of medicine. The traditional drug discovery approach involves many years of rigorous research and development and demands for a huge investment which cannot be adopted for the ongoing pandemic infection. Rather we need a swift and cost-effective approach to inhibit and control the viral infection. With the help of computational screening approaches and by choosing appropriate chemical space, it is possible to identify lead drug-like compounds for Covid-19. In this study, we have used the Drugbank database to screen compounds against the most important viral targets namely 3C-like protease (3CLpro), papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp) and the spike (S) protein. These targets play a major role in the replication/transcription and host cell recognition, therefore, are vital for the viral reproduction and spread of infection. As the structure based computational screening approaches are more reliable, we used the crystal structures for 3C-like main protease and spike protein. For the remaining targets, we used the structures based on homology modeling. Further, we employed two scoring methods based on binding free energies implemented in AutoDock Vina and molecular mechanics-generalized Born surface area approach. Based on these results, we propose drug cocktails active against the three viral targets namely 3CLpro, PLpro and RdRp. Interestingly, one of the identified compounds in this study i.e. Baloxavir marboxil has been under clinical trial for the treatment of Covid-19 infection. In addition, we have identified a few compounds such as Phthalocyanine, Tadalafil, Lonafarnib, Nilotinib, Dihydroergotamine, R-428 which can bind to all three targets simultaneously and can serve as multi-targeting drugs. Our study also included calculation of binding energies for various compounds currently under drug trials. Among these compounds, it is found that Remdesivir binds to targets, 3CLpro and RdRp with high binding affinity. Moreover, Baricitinib and Umifenovir were found to have superior target-specific binding while Darunavir is found to be a potential multi-targeting drug. As far as we know this is the first study where the compounds from the Drugbank database are screened against four vital targets of SARS-CoV-2 and illustrates that the computational screening using a double scoring approach can yield potential drug-like compounds against Covid-19 infection.

Indexed as

Databases, PharmaceuticalMolecular Targeted TherapyCoronavirus 3C ProteasesCoronavirus InfectionsCost-Benefit AnalysisCOVID-19Cysteine EndopeptidasesDrug Evaluation, PreclinicalHumansMolecular Docking SimulationPandemicsPneumonia, ViralProtein ConformationViral Nonstructural ProteinsCoronavirus 3C ProteasesCysteine EndopeptidasesViral Nonstructural Proteins

Identifiers

PMID33154404
PMCPMC7645721

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.