Evidence map›Paper›PMID 41041646›Full record

ArticleFrontiers in pharmacology2025

Evaluation of silvestrol as a potential therapeutic agent for pediatric COVID-19: an interpreted computational and phytochemistry approach.

Yi Zhang, Shanshan Pu, Hui Wang

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. SARS-CoV-2 Mpro inhibitors fromFrontiers in chemistry · 2026
    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

3 authors.

Yi Zhang *Department of Pediatrics, Pu'er People's Hospital, Pu'er, China.
Shanshan Pu *Department of Pediatrics, Pu'er People's Hospital, Pu'er, China.
Hui WangDepartment of Pediatrics, Pu'er People's Hospital, Pu'er, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The persistent COVID-19 disease, induced by SARS-CoV-2, sparked great questions about the safety and efficacy of the existing therapies in pediatric patients. The currently available antiviral drugs for treating COVID-19, either remdesivir or monoclonal antibodies, are primarily designed for adults. In many cases, their development has been hindered by concerns about safety and pediatric populations. Objectives: In the present study, we consider Silvestrol, a natural product derived from Methods: The molecular docking studies revealed that Silvestrol exhibits a highly competitive binding affinity of -7.5 kcal/mol with the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein, indicating that it may inhibit viral entry. In order to learn more about the dynamics of this interaction, the molecular dynamics (MD) simulations were carried out, which proved that the protein was stabilized in 150 ns, whereas the ligand showed conformational changes to be fit in the binding pocket, and finally stabilizing. The characterization of the pharmacophore also revealed important interaction points, including four hydrogen bond donors, 12 hydrogen bond acceptors, and eight hydrophobic sites, which increase its binding potential. Results: The favorable ADMET analysis predicted the pharmacokinetic properties of Silvestrol, which exhibited tumor-killing characteristics Conclusion: The observations suggest that Silvestrol is a promising agent for treating COVID-19 in children, as it exhibits a potent antiviral effect, low toxicity, and favorable pharmacokinetics. Further preclinical and clinical testing is needed to demonstrate its effectiveness and safety in children.

Indexed as

admetCOVID-19DFTMD simulationmolecular dockingsilvestrol

Identifiers

PMID41041646
PMCPMC12484239

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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.