| Title |
A network pharmacology approach to discover active compounds and action mechanisms of San-Cao Granule for treatment of liver fibrosis
|
|---|---|
| Published in |
Drug Design, Development and Therapy, February 2016
|
| DOI | 10.2147/dddt.s96964 |
| Pubmed ID | |
| Authors |
Shizhang Wei, Ming Niu, Jian Wang, Jiabo Wang, Haibin Su, Shengqiang Luo, Xiaomei Zhang, Yanlei Guo, Liping Liu, Fengqun Liu, Qingguo Zhao, Hongge Chen, Xiaohe Xiao, Pan Zhao, Yanling Zhao |
| Abstract |
San-Cao Granule (SCG) has been used in patients with liver fibrosis for many years and has shown good effect. However, its mechanism of therapeutic action is not clear because of its complex chemical system. The purpose of our study is to establish a comprehensive and systemic method that can predict the mechanism of action of SCG in antihepatic fibrosis. In this study, a "compound-target-disease" network was constructed by combining the SCG-specific and liver fibrosis-specific target proteins with protein-protein interactions, and network pharmacology was used to screen out the underlying targets and mechanisms of SCG for treatment of liver fibrosis. Then, some key molecules of the enriched pathway were chosen to verify the effects of SCG on liver fibrosis induced by thioacetamide (TAA). This systematic approach had successfully revealed that 16 targets related to 11 SCG compounds were closely associated with liver fibrosis therapy. The pathway-enrichment analysis of them showed that the TGF-β1/Smad signaling pathway is relatively important. Animal experiments also proved that SCG could significantly ameliorate liver fibrosis by inhibiting the TGF-β1/Smad pathway. SCG could alleviate liver fibrosis through the molecular mechanisms predicted by network pharmacology. Furthermore, network pharmacology could provide deep insight into the pharmacological mechanisms of Chinese herbal formulas. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 7% |
| Unknown | 13 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 4 | 29% |
| Librarian | 1 | 7% |
| Student > Ph. D. Student | 1 | 7% |
| Student > Bachelor | 1 | 7% |
| Researcher | 1 | 7% |
| Other | 1 | 7% |
| Unknown | 5 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 3 | 21% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 14% |
| Medicine and Dentistry | 1 | 7% |
| Chemistry | 1 | 7% |
| Engineering | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 6 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 March 2016.
All research outputs
#17,628,251
of 25,838,141 outputs
Outputs from Drug Design, Development and Therapy
#1,131
of 2,284 outputs
Outputs of similar age
#250,034
of 408,934 outputs
Outputs of similar age from Drug Design, Development and Therapy
#44
of 90 outputs
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