Evidence map›Paper›PMID 33711981›Full record

ArticleBMC biotechnology2021

Repurposing novel therapeutic candidate drugs for coronavirus disease-19 based on protein-protein interaction network analysis.

Masoumeh Adhami, Balal Sadeghi, Ali Rezapour, Ali Akbar Haghdoost, Habib MotieGhader

Open access · goldAbstract read
In one paragraph

Article in BMC biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
6.0field-weighted citation impact, top 3% 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

21 citing papers in PubMed, 43 citations in OpenAlex.

  1. Current clinical applications and future developments of platinum-based anticancer drugs.Journal of biological inorganic chemistry : JBIC : a publication of the Society of Biological Inorganic Chemistry · 2026
    Review
  2. Article
  3. Network-based drug repurposing for schizophrenia.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024
    Article
  4. Article
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  6. Small molecules in the treatment of COVID-19.Signal transduction and targeted therapy · 2022
    Review
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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

5 authors at 4 institutions in 1 country.

Masoumeh AdhamiPathology and Stem Cell Research Center, Kerman University of Medical Sciences, Kerman, Iran.
Balal SadeghiFood Hygiene and Public Health Department, Faculty of Veterinary Medicine, Shahid Bahonar University of Kerman, Kerman, Iran.
Ali RezapourDepartment of Agriculture, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
Ali Akbar HaghdoostModeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
Habib MotieGhaderDepartment of Basic sciences, Biotechnology Research Center, Tabriz Branch, Islamic Azad University, Tabriz, Iran. habib_moti@ut.ac.ir.
Kerman University of Medical Sciences · IRBiotechnology Research Center · IRIslamic Azad University of Tabriz · IRShahid Bahonar University of Kerman · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe coronavirus disease-19 (COVID-19) emerged in Wuhan, China and rapidly spread worldwide. Researchers are trying to find a way to treat this disease as soon as possible. The present study aimed to identify the genes involved in COVID-19 and find a new drug target therapy. Currently, there are no effective drugs targeting SARS-CoV-2, and meanwhile, drug discovery approaches are time-consuming and costly. To address this challenge, this study utilized a network-based drug repurposing strategy to rapidly identify potential drugs targeting SARS-CoV-2. To this end, seven potential drugs were proposed for COVID-19 treatment using protein-protein interaction (PPI) network analysis. First, 524 proteins in humans that have interaction with the SARS-CoV-2 virus were collected, and then the PPI network was reconstructed for these collected proteins. Next, the target miRNAs of the mentioned module genes were separately obtained from the miRWalk 2.0 database because of the important role of miRNAs in biological processes and were reported as an important clue for future analysis. Finally, the list of the drugs targeting module genes was obtained from the DGIDb database, and the drug-gene network was separately reconstructed for the obtained protein modules.

resultsBased on the network analysis of the PPI network, seven clusters of proteins were specified as the complexes of proteins which are more associated with the SARS-CoV-2 virus. Moreover, seven therapeutic candidate drugs were identified to control gene regulation in COVID-19. PACLITAXEL, as the most potent therapeutic candidate drug and previously mentioned as a therapy for COVID-19, had four gene targets in two different modules. The other six candidate drugs, namely, BORTEZOMIB, CARBOPLATIN, CRIZOTINIB, CYTARABINE, DAUNORUBICIN, and VORINOSTAT, some of which were previously discovered to be efficient against COVID-19, had three gene targets in different modules. Eventually, CARBOPLATIN, CRIZOTINIB, and CYTARABINE drugs were found as novel potential drugs to be investigated as a therapy for COVID-19.

conclusionsOur computational strategy for predicting repurposable candidate drugs against COVID-19 provides efficacious and rapid results for therapeutic purposes. However, further experimental analysis and testing such as clinical applicability, toxicity, and experimental validations are required to reach a more accurate and improved treatment. Our proposed complexes of proteins and associated miRNAs, along with discovered candidate drugs might be a starting point for further analysis by other researchers in this urgency of the COVID-19 pandemic.

Indexed as

Drug RepositioningProtein Interaction MapsAntiviral AgentsComputational BiologyCOVID-19 Drug TreatmentDrug DiscoveryHumansMicroRNAsSARS-CoV-2Antiviral AgentsMicroRNAs

Identifiers

PMID33711981
PMCPMC7952507
OpenAlexW3111043532

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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.