Evidence map›Paper›PMID 40738908›Full record

ArticleScientific reports2025

Comparative analysis of phytocompounds and repurposed drugs against dengue virus serotypes employing an in silico study.

Pranjal Vats, Rajan Rolta, Deeksha Salaria, Ishika Sharma, Anita Verma, Olatomide A Fadare, Mansi Verma

Erratum issuedAbstract readComparative Study
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Pranjal Vats *School of Biological Sciences, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
Rajan Rolta *Department of Pharmacology, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Deeksha SalariaDepartment of Pharmacology, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Ishika SharmaSri Venkateswara College, University of Delhi, New Delhi, 110021, India.
Anita VermaSri Venkateswara College, University of Delhi, New Delhi, 110021, India.
Olatomide A FadareOrganic Chemistry Research Lab, Department of Chemistry, ObafemiAwolowo University, Ile-Ife, Osun, 220282, Nigeria. tofadare@oauife.edu.ng.
Mansi VermaDepartment of Zoology, Hansraj College, University of Delhi, Delhi, 110007, India. mansiverma@hrc.du.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dengue virus (DENV) has emerged as a formidable global health challenge, with a surging incidence rate across the world. Despite numerous research initiatives aimed at developing effective antiviral therapy, no clinically proven drug or vaccine has been identified to combat all four genetically diverse serotypes of DENV. Therefore, comparative analysis of repurposed drugs and phytocompounds against all DENV serotypes is critical in the search for an effective long-term solution to this menacing disease. 93 phytocompounds and 15 drugs were shortlisted from the literature and screened using DataWarrior 5.5.0, from which 10 phytocompounds and 10 drugs were selected for further analysis. Molecular docking was performed by using AutoDockVina tool. Toxicity and drug likeness activity of standard drugs and phytoconstituents was done by using online servers. The current study showed that among all the selected phytoconstituents, lupiwighteone showed the best binding energy, favorable pharmacokinetics and no toxicity with all the selected serotypes of DENV. The MD simulation result supported the stability of lupiwighteone in complexes with NS3, NS5 and E-Protein. This study identifies lupiwighteone as a promising antiviral candidate with favorable drug-like properties against DENV-2, DENV-3, and DENV-4 serotypes. Furthermore, in vitro and in vivo study is required for the validation of antiviral activity of Lupiwighteone against dengue virus.

Indexed as

Antiviral AgentsDengue VirusDrug RepositioningPhytochemicalsComputer SimulationDengueHumansMolecular Docking SimulationMolecular Dynamics SimulationSerogroupViral Nonstructural ProteinsAntiviral AgentsPhytochemicalsViral Nonstructural ProteinsDENVMD simulationPhytocompoundsRepurposed drugsSerotypes

Identifiers

PMID40738908
PMCPMC12311024

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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.