Evidence map›Paper›PMID 39949090›Full record

ArticleCurrent pharmaceutical design2025

Systems Pharmacology-based Drug Discovery and Active Mechanism of

Junkai Shi, Jialiang Chen, Chitong Cheng, Wei Li, Ming Li, Shuhong Ye, Zhaofang Liu, Yan Ding

Abstract read
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In one paragraph

Article in Current pharmaceutical design, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Metabolites · 2026
    Review
  2. Biocontrol strategies for fungal diseases ofFrontiers in microbiology · 2026
    Review
  3. Frontiers in nutrition · 2025
    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.

Junkai ShiSKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China.
Jialiang ChenSKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China.
Chitong ChengSKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China.
Wei LiKorean Medicine (KM) Application Center, Korea Institute of Oriental Medicine, Daegu 41062, Korea.
Ming LiCollege of Basic Medical Science, Dalian Medical University, Dalian, Liaoning 116044, China.
Shuhong YeSKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China.
Zhaofang LiuSKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China.
Yan DingSKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China.

Funding

China Scholarship Council (CSC) 202008210171National Natural Science Foundation of China 31770725Natural Science Foundation of Liaoning Province 2021-MS-299
6 · The paper itself

Abstract

backgroundType 2 Diabetes Mellitus (T2DM) is a chronic metabolic disease primarily characterized by insufficient insulin secretion or reduced insulin sensitivity in the body's cells, leading to persistently high blood glucose levels.

objectiveIn the present research, we aim to fully employ the integrated approach of network pharmacology and molecular docking methodologies, delving deeply into the potential therapeutic targets and their underlying pharmacological mechanisms in the management of T2DM

methodsThe active compounds were sourced from prior research and the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Their potential targets were predicted with the aid of Swiss Target Prediction. Genes linked to T2DM were gathered from DisGeNET and GeneCards. Using Cytoscape, we established the network connecting active ingredients, targets, and pathways, and the target protein-protein interaction (PPI) network was created using data from the STRING database. The core targets of

resultsA total of 53 Ganoderma lucidum triterpenoids and 116 associated targets were identified. Among these, SRC, MAPK1, MAPK3, HSP90AA1, TP53, PIK3CA, and AKT1 emerged as pivotal targets. We retrieved 447 Gene Ontology (GO) functional annotations and 153 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, notably including the PI3K-Akt signaling pathway, Endocrine resistance, Rap1 signaling pathway, and Lipid and Atherosclerosis, which are known to be associated with T2DM. Our findings suggest that

conclusionA comprehensive, interdisciplinary, and multi-technology approach has been established, which uncovers the collaborative effects and underlying principles of

Indexed as

Diabetes Mellitus, Type 2Drug DiscoveryHypoglycemic AgentsMolecular Docking SimulationNetwork PharmacologyReishiTriterpenesHumansHypoglycemic AgentsTriterpenesGanoderma lucidum triterpenoidsmolecular docking.network analysisNetwork pharmacologysignaling pathwaystype 2 diabetes mellitus

Identifiers

PMID39949090

What OpenQuestion holds

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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.