Evidence map›Paper›PMID 32754224›Full record

ArticleChinese medicine2020

Potential mechanism prediction of Cold-Damp Plague Formula against COVID-19 via network pharmacology analysis and molecular docking.

Lin Han, Xiu-Xiu Wei, Yu-Jiao Zheng, Li-Li Zhang, Xin-Miao Wang, Hao-Yu Yang, Xu Ma, Lin-Hua Zhao, Xiao-Lin Tong

Abstract read
In one paragraph

Article in Chinese medicine, 2020. 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
–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

21 citing papers in PubMed.

  1. Article
  2. Article
  3. Exploring the Therapeutic Potential ofMolecules (Basel, Switzerland) · 2024
    Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. Bioactive compounds as potential angiotensin-converting enzyme II inhibitors against COVID-19: a scoping review.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2022
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Review
  17. Article
  18. Traditional Chinese medicine in COVID-19.Acta pharmaceutica Sinica. B · 2021
    Review
  19. The direct evidence and mechanism of traditional Chinese medicine treatment of COVID-19.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2021
    Review
  20. Review
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

9 authors.

Lin Han *Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053 China.
Xiu-Xiu Wei *Beijing University of Chinese Medicine, Beijing, 100029 China.
Yu-Jiao Zheng *Beijing University of Chinese Medicine, Beijing, 100029 China.
Li-Li ZhangGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053 China.
Xin-Miao WangGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053 China.
Hao-Yu YangBeijing University of Chinese Medicine, Beijing, 100029 China.
Xu MaGansu University of Chinese Medicine, Lanzhou, 730000 China.
Lin-Hua ZhaoGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053 China.
Xiao-Lin TongGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCoronavirus disease 2019 (COVID-19) is a new global public health emergency. The therapeutic benefits of Cold‒Damp Plague Formula (CDPF) against COVID-19, which was used to treat "cold‒dampness stagnation in the lung" in Trial Versions 6 and 7 of the "Diagnosis and Treatment Protocol for COVID-19", have been demonstrated, but the effective components and their mechanism of action remain unclear.

methodsIn this study, a network pharmacology approach was employed, including drug-likeness evaluation, oral bioavailability prediction, protein‒protein interaction (PPI) network construction and analysis, Gene Ontology (GO) terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation, and virtual docking, to predict the bioactive components, potential targets, and molecular mechanism of CDPF for COVID-19 treatment.

resultsThe active compound of herbs in CDPF and their candidate targets were obtained through database mining, and an herbs-ingredients-targets network was constructed. Subsequently, the candidate targets of the active compounds were compared to those relevant to COVID-19, to identify the potential targets of CDPF for COVID-19 treatment. Subsequently, the PPI network was constructed, which provided a basis for cluster analysis and hub gene screening. The seed targets in the most significant module were selected for further functional annotation. GO enrichment analysis identified four main areas: (1) cellular responses to external stimuli, (2) regulation of blood production and circulation, (3) free radical regulation, (4) immune regulation and anti-inflammatory effects. KEGG pathway analysis also revealed that CDPF could play pharmacological roles against COVID-19 through "multi components‒multi targets‒multi pathways" at the molecular level, mainly involving anti-viral, immune-regulatory, and anti-inflammatory pathways; consequently, a "CDPF-herbs-ingredients-targets-pathways-COVID-19" network was constructed. In hub target analysis, the top hub target IL6, and ACE2, the receptor via which SARS-CoV-2 typically enters host cells, were selected for molecular docking analyses, and revealed good binding activities.

conclusionsThis study revealed the active ingredients and potential molecular mechanism by which CDPF treatment is effective against COVID-19, and provides a reference basis for the wider application and further mechanistic investigations of CDPF in the fight against COVID-19.

Indexed as

Cold‒Damp Plague Formula (CDPF)COVID-19Molecular dockingMolecular mechanismNetwork pharmacology

Identifiers

PMID32754224
PMCPMC7391051

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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.