Network Pharmacology and Molecular Docking-Based Strategy to Investigate the Multitarget Mechanisms of Shenqi Yizhi Granule on Alzheimer's Disease.

Abstract

Background:

Traditional Chinese herbal medicine draws more attention to explore an effective therapeutic strategy for Alzheimer's disease (AD). Shenqi Yizhi granule (SQYG), a Chinese herbal recipe, has been applied to ameliorate cognitive impairment in mild-to-moderate AD patients. However, the overall molecular mechanism of SQYG in treating AD has not been clarified.

Objective:

This study aimed to investigate the molecular mechanism of SQYG on AD using an integration strategy of network pharmacology and molecular docking.

Methods:

The active compounds of SQYG and common targets between SQYG and AD were screened from databases. The herb-compound network, compound-target network, and protein-protein interaction network were constructed. The enrichment analysis of common targets and molecular docking were performed.

Results:

816 compounds and 307 common targets between SQYG and AD were screened. KEGG analysis revealed that common targets were mainly enriched in lipid metabolism, metal ion metabolism, IL-17 signaling pathway, GABA receptor signaling, and neuroactive ligand-receptor interaction. Molecular docking analysis showed high binding affinity between ginsenoside Rg1 and Aβ 1-42, tanshinone IIA and BACE1, baicalin, and AchE.

Conclusions:

The therapeutic mechanisms of SQYG on AD were associated with regulating lipid metabolism, metal ion metabolism, IL-17 signaling pathway, and GABA receptor signaling. Ginsenoside Rg1, tanshinone IIA, baicalin, astragaloside IV, and folic acid may play an important role in AD treatment.

• Authors: Linshuang Wang, Xiaoyu Xu, Zikang Wang, Qian Chen, Xiaodie Wei, Jingfan Xue, Zhanjun Zhang, Miao Wang, Yanping Li, Junying Zhang, Dongfeng Wei
• Journal: Evidence-based complementary and alternative medicine : eCAM (Evid Based Complement Alternat Med) Vol. 2022 Pg. 8032036 ( 2022) ISSN: 1741-427X [Print] United States
• PMID: 35535155 (Publication Type: Journal Article)
• Copyright: Copyright © 2022 Linshuang Wang et al.