Evidence map›Paper›PMID 34367309›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2021

Network Pharmacological Study and Molecular Docking Analysis of Qiweitangping in Treating Diabetic Coronary Heart Disease.

Tianfu Liu, Jianbo Liu, Liang Hao

Open access · hybridAbstract read
In one paragraph

Article in Evidence-based complementary and alternative medicine : eCAM, 2021. 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
0.2field-weighted citation impact, top 51% of its field
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Tianfu LiuChina Medical University-The Queen's University of Belfast Joint Collage, China Medical University, Shenyang 110122, Liaoning Province, China.ORCID https://orcid.org/0000-0001-5501-0706
Jianbo LiuShanxi Three-Principles & Six-Disease Institute of Traditional Chinese Medicine, Taiyuan 030001, Shanxi Province, China.
Liang HaoChina Medical University-The Queen's University of Belfast Joint Collage, China Medical University, Shenyang 110122, Liaoning Province, China.
China Medical University · CNShanxi Province Hospital of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCoronary heart disease (CHD) is one of the most important complications of diabetes mellitus, having a high disability fatality rate. Qiweitangping is a Chinese medicine to control diabetes (type 1 and type 2 diabetes) and complications, having been used in clinic for more than 20 years, with the expected therapeutic effect. In a previous clinical study, the total effective rate of the drug for the treatment of type 2 diabetes reached 92.7%. However, the mechanism of the treatment process is unclear. Therefore, this research was conducted to explore the mechanism of treating diabetic coronary heart disease with the assistance of bioinformatics methods.

methodsThe TCMSP database was used to collect the effective chemical constituents of Qiweitangping and the target genes of the chemical constituents, and the related genes of diabetic CHD were obtained from the GeneCard database. Furthermore, the intersection was found between the target gene of the drug and the related gene of the disease to obtain the candidate genes; the STRING database and DAVID database were used to perform protein interaction analysis and KEGG enrichment analysis on the candidate genes. Also, molecular docking was used for auxiliary verification. Finally, a "drug component-gene target-pathway" network was constructed by using Cytoscape software.

resultsSixty-two effective chemical components including naringin, diosgenin, formogenin, isorolin, and isocryptanshinone, fifty-nine candidate target genes (such as AKT1, CASP3, and VEGF-A), and thirty-nine related pathways in Qiweitangping were obtained. In addition, two pairs (CASP-naringenin and STAT3-cryptotanshinone) of molecular docking results showed good affinity (<-5.00 kcal/mol).

conclusionThe results of the study indicate that Qiweitangping treats diabetic CHD with multiple chemical components. Its mechanism of action may be related to the HIF-1 signaling pathway, PI3K-Akt signaling pathway, and ErbB signaling pathway. STAT3 and CASP genes have been verified by molecular docking to have a relatively good combination with Qiweitangping. This study is the theoretical basis for further experimental study on the treatment mechanism of diabetic CHD with Qiweitangping.

Identifiers

PMID34367309
PMCPMC8337130
OpenAlexW3186149842

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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.