Studies on the Mechanism of Gegen Qinlian Decoction in Treating Diabetes Mellitus Based on Network Pharmacology

Abstract

Diabetes mellitus (DM) is a chronic disease that is very common and seriously threatens patient health. Gegen Qinlian decoction (GQD) has long been applied clinically, but its mechanism in pharmacology has not been extensively and systematically studied. A GQD protein interaction network and diabetes protein interaction network were constructed based on the methods of system biology. Functional module analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and Gene Ontology (GO) enrichment analysis were carried out on the 2 networks. The hub nodes were filtered by comparative analysis. The topological parameters, interactions, and biological functions of the 2 networks were analyzed in multiple ways. By applying GEO-based external datasets to verify the results of our analysis that the Gene Set Enrichment Analysis (GSEA) displayed metabolic pathways in which hub genes played roles in regulating different expression states. Molecular docking is used to verify the effective components that can be combined with hub nodes. By comparing the 2 networks, 24 hub targets were filtered. There were 7 complex relationships between the networks. The results showed 4 topological parameters of the 24 selected hub targets that were much higher than the median values, suggesting that these hub targets show specific involvement in the network. The hub genes were verified in the GEO database, and these genes were closely related to the biological processes involved in glucose metabolism. Molecular docking results showed that 5,7,2', 6'-tetrahydroxyflavone, magnograndiolide, gancaonin I, isoglycyrol, gancaonin A, worenine, and glyzaglabrin produced the strongest binding effect with 10 hub nodes. This compound–target mode of interaction may be the main mechanism of action of GQD. This study reflected the synergistic characteristics of multiple targets and multiple pathways of traditional Chinese medicine and discussed the mechanism of GQD in the treatment of DM at the molecular pharmacological level.

Computer-Aided Screening of Phytoconstituents from Ocimum tenuiflorum against Diabetes Mellitus Targeting DPP4 Inhibition: A Combination of Molecular Docking, Molecular Dynamics, and Pharmacokinetics Approaches

“…Since the binding interactions of these Schiff's based compounds and O. tenuiflorum compounds selected from our study are similar, the latter could act as the potential inhibitors of the enzyme. Therefore, employing 1S-α-pinene, β-pinene, and dehydro-p-cymene could bring up the essential biological inhibition of DPP4 [39,40]. In a similar approach, three phytochemicals isolated from the seeds of Lens culinaris screened against DPP4, but the study did not include molecular dynamics simulation and binding free energy calculations to validate their lead compounds [41].…”

Analysis of Chemical Changes during Maturation of Amomum tsao-ko Based on GC-MS, FT-NIR, and FT-MIR

“…The structure of the network is shown in Figure , which consists of 27 compounds, 85 key targets, 20 important signaling pathways, and 1 disease (obesity), forming a “component–target–disease–pathway” network with 132 nodes and 508 edges. From the network, it can be observed that each component corresponds to more targets, and the targets are connected with different signal pathways, indicating that A. tsao-ko has the characteristics of “multi-component, multi-target, and multi-pathway” in the treatment or adjuvant treatment of obesity, and there is synergy between different treatment pathways. , Among them, decanoic acid, metal ester (NO. 50), nonanoic acid, methyl ester (NO.…”

“…From the network, it can be observed that each component corresponds to more targets, and the targets are connected with different signal pathways, indicating that A. tsao-ko has the characteristics of “multi-component, multi-target, and multi-pathway” in the treatment or adjuvant treatment of obesity, and there is synergy between different treatment pathways. 27 , 51 Among them, decanoic acid, metal ester (NO. 50), nonanoic acid, methyl ester (NO.…”