Evidence map›Paper›PMID 29934492›Full record

ArticleMedical science monitor : international medical journal of experimental and clinical research2018

A Systems Biology-Based Approach to Uncovering Molecular Mechanisms Underlying Effects of Traditional Chinese Medicine Qingdai in Chronic Myelogenous Leukemia, Involving Integration of Network Pharmacology and Molecular Docking Technology.

Chao Zhou, LiJuan Liu, Jing Zhuang, JunYu Wei, TingTing Zhang, ChunDi Gao, Cun Liu, HuaYao Li, HongZong Si, ChangGang Sun

Open access · hybridAbstract read
In one paragraph

Article in Medical science monitor : international medical journal of experimental and clinical research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
3.4field-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

22 citing papers in PubMed, 34 citations in OpenAlex.

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  18. Applications of Network Pharmacology in Traditional Chinese Medicine Research.Evidence-based complementary and alternative medicine : eCAM · 2020
    Review
  19. Article
  20. 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

10 authors at 4 institutions in 1 country.

Chao ZhouCancer Center, WeiFang Traditional Chinese Hospital, Weifang, Shandong, China (mainland).
LiJuan LiuCancer Center, WeiFang Traditional Chinese Hospital, Weifang, Shandong, China (mainland).
Jing ZhuangCancer Center, WeiFang Traditional Chinese Hospital, Weifang, Shandong, China (mainland).
JunYu WeiCancer Center, WeiFang Traditional Chinese Hospital, Weifang, Shandong, China (mainland).
TingTing ZhangClinical Institute, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China (mainland).
ChunDi GaoClinical Institute, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China (mainland).
Cun LiuClinical Institute, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China (mainland).
HuaYao LiClinical Institute, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China (mainland).
HongZong SiDepartment of Public Health, Qingdao University, Qingdao, Shandong, China (mainland).
ChangGang SunDepartment of Oncology, Affiliated Hospital of Weifang Medical University, Weifang, Shandong, China (mainland).
Shandong University of Traditional Chinese Medicine · CNWeifang Chinese Medicine Hospital · CNQingdao University · CNWeifang Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND The method of multiple targets overall control is increasingly used to predict the main active ingredient and potential target group of Chinese traditional medicines and to determine the mechanisms involved in their curative effects. Qingdai is the main traditional Chinese medicine used in the treatment of chronic myelogenous leukemia (CML), but the complex active ingredients and antitumor targets in treatment of CML have not been clearly defined in previous studies. MATERIAL AND METHODS We constructed a protein-protein interaction network diagram of CML with 638 nodes (proteins) and 1830 edges, based on the biological function of chronic myelocytic leukemia by use of Cytoscape, and we determined 19 key gene nodes in the CML molecule by network topological properties analysis in a data bank. Then, we used the Surflex-dock plugin in SYBYL7.3 docking and acquired the protein crystal structures of key genes involved in CML from the chemical composition of the traditional Chinese medicine Qingdai with key proteins in CML networks. RESULTS According to the score and the spatial structure, the pharmacodynamically active ingredients of Qingdai are Isdirubin, Isoindigo, N-phenyl-2-naphthylamine, and Isatin, among which Isdirubin is the most important. We further screened the most effective activity key protein structures of CML to find the best pharmacodynamically active ingredients of Qingdai, according to the binding interactions of the inhibitors at the catalytic site performed in best docking combinations. CONCLUSIONS The results suggest that Isdirubin plays a role in resistance to CML by altering the expressions of PIK3CA, MYC, JAK2, and TP53 target proteins. Network pharmacology and molecular docking technology can be used to search for possible reactive molecules in traditional chinese medicines (TCM) and to elucidate their molecular mechanisms.

Indexed as

Medicine, Chinese TraditionalMolecular Docking SimulationDrug Evaluation, PreclinicalDrugs, Chinese HerbalGene Expression Regulation, LeukemicHumansLeukemia, Myelogenous, Chronic, BCR-ABL PositiveLigandsNeoplasm ProteinsProtein Interaction MapsSystems BiologyDrugs, Chinese HerbalLigandsNeoplasm ProteinsQingdai compound

Identifiers

PMID29934492
PMCPMC6049014
OpenAlexW2809815361

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.