Evidence map›Paper›PMID 34099831›Full record

ArticleScientific reports2021

A metabolic modeling approach reveals promising therapeutic targets and antiviral drugs to combat COVID-19.

Fernando Santos-Beneit, Vytautas Raškevičius, Vytenis A Skeberdis, Sergio Bordel

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
8.1field-weighted citation impact, top 2% of its field
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

27 citing papers in PubMed, 51 citations in OpenAlex.

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  16. COVID-19 therapies: do we see substantial progress?Cellular & molecular biology letters · 2022
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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 at 2 institutions in 2 countries.

Fernando Santos-BeneitInstitute of Sustainable Processes, Universidad de Valladolid, Valladolid, Spain.ORCID http://orcid.org/0000-0001-7986-5557
Vytautas RaškevičiusCell Culture Laboratory, Institute of Cardiology, Lithuanian University of Health Sciences, Kaunas, Lithuania.
Vytenis A SkeberdisCell Culture Laboratory, Institute of Cardiology, Lithuanian University of Health Sciences, Kaunas, Lithuania.
Sergio BordelInstitute of Sustainable Processes, Universidad de Valladolid, Valladolid, Spain. sergio.bordel@uva.es.ORCID http://orcid.org/0000-0001-6162-6478
Lithuanian University of Health Sciences · LTUniversidad de Valladolid · ES

Funding

Lithuanian Research Council S-SEN-20-6Regional Government of Castilla y León CLU 2017-09
6 · The paper itself

Abstract

In this study we have developed a method based on Flux Balance Analysis to identify human metabolic enzymes which can be targeted for therapeutic intervention against COVID-19. A literature search was carried out in order to identify suitable inhibitors of these enzymes, which were confirmed by docking calculations. In total, 10 targets and 12 bioactive molecules have been predicted. Among the most promising molecules we identified Triacsin C, which inhibits ACSL3, and which has been shown to be very effective against different viruses, including positive-sense single-stranded RNA viruses. Similarly, we also identified the drug Celgosivir, which has been successfully tested in cells infected with different types of viruses such as Dengue, Zika, Hepatitis C and Influenza. Finally, other drugs targeting enzymes of lipid metabolism, carbohydrate metabolism or protein palmitoylation (such as Propylthiouracil, 2-Bromopalmitate, Lipofermata, Tunicamycin, Benzyl Isothiocyanate, Tipifarnib and Lonafarnib) are also proposed.

Indexed as

COVID-19 Drug TreatmentMolecular Docking SimulationAntiviral AgentsDengue VirusHepacivirusSARS-CoV-2Virus ReplicationZika VirusZika Virus InfectionAntiviral Agents

Identifiers

PMID34099831
PMCPMC8184994
OpenAlexW3168267134

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.