Exploring the effect and mechanism of Shaoyao Gancao Decoction in the treatment of pain in Parkinson's disease using network pharmacology and molecular docking

IF 2.9 Q3 NEUROSCIENCES IBRO Neuroscience Reports Pub Date : 2025-06-01 Epub Date: 2025-01-20 DOI:10.1016/j.ibneur.2025.01.013
Zhaohui Xu , Qing Zhao
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Abstract

This study explores the potential effects and mechanisms of SGD in treating pain in PD based on network pharmacology and molecular docking technology.The chemical components in the aqueous extract from SGD were identified using GC-MS analysis. A prediction network describing the relationship between SGD and pain in PD was established based on information collected from multiple databases. Using Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Geomes (KEGG) pathway enrichment of key target genes in the DAVID6.8 database to obtain treatment target genes. To further investigate the molecular interactions, AutoDock Vina were employed to perform molecular docking and visualize the resulting outcomes. There were 206 targets obtained from the 105 active ingredients in Paeoniae Radix Alba and Radix Rhizoma Glycyrrhizae, and 5110 disease targets related to pain in PD were identified. GO enrichment analysis indicates that its Biologica Process (BP) involve response to lipopolysaccharide, response to metal ion. Cellular Component (CC) analysis suggests its primary impact on various membrane structural components. Molecular Function (MF) enrichment results primarily include ubiquitin-like protein ligase binding. KEGG pathway enrichment mainly encompasses MAPK, AGE-RAGE, IL-17, TNF, and Toll-like receptor signaling pathways. According to the results of molecular docking, the binding activity between core components and targets was marvelous (affinity < −5.0 kcal/mol). SGD has more advantages in the regulation of various types of pain in PD through multiple targets, which is worthy of further study.
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应用网络药理学与分子对接探讨少药干草汤治疗帕金森病疼痛的作用及机制
本研究基于网络药理学和分子对接技术,探索SGD治疗PD疼痛的潜在作用和机制。采用气相色谱-质谱联用分析方法对水提物中的化学成分进行了鉴定。基于多个数据库收集的信息,建立了描述PD中SGD与疼痛关系的预测网络。利用基因本体(GO)富集和京都基因与基因组百科全书(KEGG)途径富集DAVID6.8数据库中的关键靶基因获得治疗靶基因。为了进一步研究分子间的相互作用,使用AutoDock Vina进行分子对接并将结果可视化。从白芍和甘草的105种有效成分中获得206个靶点,鉴定出与PD疼痛相关的疾病靶点5110个。氧化石墨烯富集分析表明其生物过程包括对脂多糖的响应、对金属离子的响应。细胞成分(CC)分析表明其主要影响各种膜结构成分。分子功能(MF)富集结果主要包括泛素样蛋白连接酶结合。KEGG通路富集主要包括MAPK、AGE-RAGE、IL-17、TNF和toll样受体信号通路。根据分子对接结果,核心组分与靶点之间的结合活性极好(亲和性<;−5.0 千卡每摩尔)。SGD通过多靶点调控PD的各类疼痛更有优势,值得进一步研究。
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来源期刊
IBRO Neuroscience Reports
IBRO Neuroscience Reports Neuroscience-Neuroscience (all)
CiteScore
2.80
自引率
0.00%
发文量
99
审稿时长
14 weeks
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