Evidence map›Paper›PMID 31354851›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2019

Network Pharmacology-Based Prediction of the Active Compounds, Potential Targets, and Signaling Pathways Involved in Danshiliuhao Granule for Treatment of Liver Fibrosis.

Yueying Tao, Kunming Tian, Ji Chen, Danfeng Tan, Yan Liu, Yongai Xiong, Zehui Chen, Yingbiao Tian

Open access · hybridAbstract read
In one paragraph

Article in Evidence-based complementary and alternative medicine : eCAM, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
3.7field-weighted citation impact, top 7% 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

18 citing papers in PubMed, 35 citations in OpenAlex.

  1. International journal of molecular sciences · 2026
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  3. Network Pharmacology and Validation of the Combinative Therapy ofCombinatorial chemistry & high throughput screening · 2025
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  11. Exploring the Mechanism ofEvidence-based complementary and alternative medicine : eCAM · 2022
    Article
  12. Article
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  15. Article
  16. Article
  17. Article
  18. Identified the Synergistic Mechanism ofEvidence-based complementary and alternative medicine : eCAM · 2019
    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

8 authors at 2 institutions in 1 country.

Yueying TaoThe Affiliated Hospital of Zunyi Medical University, Zunyi 563000, China.ORCID https://orcid.org/0000-0001-9351-6257
Kunming TianDepartment of Preventive Medicine, School of Public Health, Zunyi Medical University, Zunyi 563003, China.
Ji ChenDepartment of Pharmacy, Zunyi Second People's Hospital, Zunyi 563000, China.
Danfeng TanSchool of Pharmacy, Zunyi Medical University, Zunyi 563003, China.
Yan LiuThe Second Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China.
Yongai XiongSchool of Pharmacy, Zunyi Medical University, Zunyi 563003, China.
Zehui ChenThe Affiliated Hospital of Zunyi Medical University, Zunyi 563000, China.ORCID https://orcid.org/0000-0003-4055-3008
Yingbiao TianThe Affiliated Hospital of Zunyi Medical University, Zunyi 563000, China.ORCID https://orcid.org/0000-0003-0303-9982
Zunyi Medical University · CNFirst People’s Hospital of Zunyi · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to predict the active ingredients, potential targets, signaling pathways and investigate the "ingredient-target-pathway" mechanisms involved in the pharmacological action of Danshiliuhao Granule (DSLHG) on liver fibrosis. Pharmacodynamics studies on rats with liver fibrosis showed that DSLHG generated an obvious anti-liver fibrosis action. On this basis, we explored the possible mechanisms underlying its antifibrosis effect using network pharmacology approach. Information about compounds of herbs in DSLHG was collected from TCMSP public database and literature. Furthermore, the oral bioavailability (OB) and drug-likeness (DL) were screened according to ADME features. Compounds with OB≥30% and DL≥0.18 were selected as active ingredients. Then, the potential targets of the active compounds were predicted by pharmacophore mapping approach and mapped with the target genes of the specific disease. The compound-target network and Protein-Protein Interaction (PPI) network were built by Cytoscape software. The core targets were selected by degree values. Furthermore, GO biological process analysis and KEGG pathway enrichment analysis were carried out to investigate the possible mechanisms involved in the anti-hepatic fibrosis effect of DSLHG. The predicted results showed that there were 108 main active components in the DSLHG formula. Moreover, there were 192 potential targets regulated by DSLHG, of which 86 were related to liver fibrosis, including AKT1, EGFR, and IGF1R. Mechanistically, the anti-liver fibrosis effect of DSLHG was exerted by interfering with 47 signaling pathways, such as PI3K-Akt, FoxO signaling pathway, and Ras signaling pathway. Network analysis showed that DSLHG could generate the antifibrosis action by affecting multiple targets and multiple pathways, which reflects the multicomponent, multitarget, and multichannel characteristics of traditional Chinese medicine and provides novel basis to clarify the mechanisms of anti-liver fibrosis of DSLHG.

Identifiers

PMID31354851
PMCPMC6636523
OpenAlexW2955633963

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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.