Evidence map›Paper›PMID 27095146›Full record

ArticleScientific reports2016

Drug target identification using network analysis: Taking active components in Sini decoction as an example.

Si Chen, Hailong Jiang, Yan Cao, Yun Wang, Ziheng Hu, Zhenyu Zhu, Yifeng Chai

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
8.3field-weighted citation impact, top 2% 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

30 citing papers in PubMed, 74 citations in OpenAlex.

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  19. The mechanism underlying hypaconitine-mediated alleviation of pancreatitis-associated lung injury through up-regulating aquaporin-1/TNF-α.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2020
    Article
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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

7 authors at 2 institutions in 2 countries.

Si ChenSchool of Pharmacy, Second Military Medical University, 325 Guohe Road, Shanghai, 200433, China.
Hailong JiangSchool of Pharmacy, Second Military Medical University, 325 Guohe Road, Shanghai, 200433, China.
Yan CaoSchool of Pharmacy, Second Military Medical University, 325 Guohe Road, Shanghai, 200433, China.
Yun WangSchool of Pharmacy, Second Military Medical University, 325 Guohe Road, Shanghai, 200433, China.
Ziheng HuSchool of Pharmacy, University of Pittsburgh, 3501 Terrace Street, Pittsburgh, PA, 15261, USA.
Zhenyu ZhuSchool of Pharmacy, Second Military Medical University, 325 Guohe Road, Shanghai, 200433, China.
Yifeng ChaiSchool of Pharmacy, Second Military Medical University, 325 Guohe Road, Shanghai, 200433, China.
Second Military Medical University · CNUniversity of Pittsburgh · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound.

Indexed as

Neural Networks, ComputerCell Line, TumorDrugs, Chinese HerbalHumansMetabolic Networks and PathwaysMetabolomicsMyocytes, CardiacProteinsReproducibility of ResultsSignal TransductionStructure-Activity RelationshipTumor Necrosis Factor-alphaDrugs, Chinese HerbalProteinssini tangTumor Necrosis Factor-alpha

Identifiers

PMID27095146
PMCPMC4837341
OpenAlexW2340326169

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.