Network Pharmacology and Validation of the Combinative Therapy of Ligusticum striatum DC. and Borneolum against Cerebral Ischemia.

IF 1.6 4区医学 Q4 BIOCHEMICAL RESEARCH METHODS

Combinatorial chemistry & high throughput screening Pub Date : 2024-09-19 DOI:10.2174/0113862073317255240902075511

Ming Ruan, Gaohong Lv, Xueqing Wang, Fengjiao Deng, Tianya Xia, Bin Yu, Shengjin Liu

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引用次数: 0

Abstract

Background: Ligusticum striatum DC. (LDC) is often prescribed for Cerebral Ischemia (CI) and is commonly combined with Borneolum (BO) to enhance therapeutic outcomes. However, its specific active ingredients and underlying mechanisms remain unclear.

Objective: This study aimed to identify the active ingredients and mechanisms of LDC and BO combination therapy against CI using network pharmacology, molecular docking, and in vivo experiments.

Methods: Potential active ingredients and targets were sourced from relevant databases, and a drug-component-target-disease network was constructed to pinpoint key ingredients. Subsequently, a protein-protein interaction analysis was conducted to confirm the key targets. Following enrichment analyses of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), molecular docking was employed to evaluate binding energies. Finally, the therapeutic effects and mechanisms of the combination against CI were validated through in vivo experiments using male ICR mice.

Results: Venn analysis identified a total of 41 components and 292 potential targets. The drugcomponent-target-disease network revealed that the key components in LDC were palmitic acid, tetramethylpyrazine, and (Z)-ligustilide, while those in BO were (+)-borneol, β-elemene, and (-)- borneol. The PPI analysis highlighted seven crucial targets. Docking results confirmed a stable affinity between these components and their targets. KEGG enrichment analysis indicated that the mechanism involved the PI3K/AKT signaling pathway. Subsequently, in vivo experiments confirmed that the combination ameliorated abnormal hippocampus morphology and reduced the release of inflammatory factors through the activation of the PI3K/AKT signaling pathway.

Conclusion: The combination of LDC and BO markedly improved CI and inhibited inflammation response via activating the PI3K/AKT pathway.

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藁本和婆婆纳联合治疗脑缺血的网络药理学和验证。

背景介绍Ligusticum striatum DC.(LDC) 是治疗脑缺血（CI）的常用处方药，通常与婆婆纳（Borneolum，BO）联合使用以提高治疗效果。然而，其具体的活性成分和潜在机制仍不清楚：本研究旨在通过网络药理学、分子对接和体内实验，确定 LDC 与婆婆纳联合治疗 CI 的活性成分和机制：方法：从相关数据库中寻找潜在的活性成分和靶点，构建药物-成分-靶点-疾病网络，找出关键成分。随后，进行了蛋白质-蛋白质相互作用分析，以确认关键靶点。在对基因本体（GO）和京都基因和基因组百科全书（KEGG）进行富集分析后，采用分子对接来评估结合能。最后，通过使用雄性 ICR 小鼠进行体内实验，验证了该组合对 CI 的治疗效果和机制：结果：维恩分析共发现了 41 种成分和 292 个潜在靶点。药物-成分-靶点-疾病网络显示，LDC 的关键成分是棕榈酸、四甲基吡嗪和 (Z)-ligustilide; BO 的关键成分是 (+)-borneol, β-榄香烯和 (-)-borneol.PPI 分析突出了七个关键靶标。对接结果证实，这些成分与其靶标之间具有稳定的亲和力。KEGG 富集分析表明，其机制涉及 PI3K/AKT 信号通路。随后的体内实验证实，通过激活 PI3K/AKT 信号通路，该组合能改善异常海马形态并减少炎症因子的释放：结论：LDC和BO的组合能显著改善CI，并通过激活PI3K/AKT通路抑制炎症反应。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Combinatorial chemistry & high throughput screening 化学-生化研究方法

CiteScore

3.10

自引率

5.60%

发文量

327

审稿时长

7.5 months

期刊介绍： Combinatorial Chemistry & High Throughput Screening (CCHTS) publishes full length original research articles and reviews/mini-reviews dealing with various topics related to chemical biology (High Throughput Screening, Combinatorial Chemistry, Chemoinformatics, Laboratory Automation and Compound management) in advancing drug discovery research. Original research articles and reviews in the following areas are of special interest to the readers of this journal: Target identification and validation Assay design, development, miniaturization and comparison High throughput/high content/in silico screening and associated technologies Label-free detection technologies and applications Stem cell technologies Biomarkers ADMET/PK/PD methodologies and screening Probe discovery and development, hit to lead optimization Combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries) Chemical library design and chemical diversity Chemo/bio-informatics, data mining Compound management Pharmacognosy Natural Products Research (Chemistry, Biology and Pharmacology of Natural Products) Natural Product Analytical Studies Bipharmaceutical studies of Natural products Drug repurposing Data management and statistical analysis Laboratory automation, robotics, microfluidics, signal detection technologies Current & Future Institutional Research Profile Technology transfer, legal and licensing issues Patents.