基于网络药理学和分子对接探究土茯苓-薏苡仁药对治疗痛风及高尿酸血症的作用机制

doi:10.5246/jcps.2025.08.055

摘要/Abstract

摘要：

本研究旨在通过网络药理学、分子对接探究土茯苓-薏苡仁药对治疗痛风及高尿酸血症的作用机制。采用GeneCards、OMIM、Disgenet和TTD数据库筛选痛风及高尿酸血症的疾病靶点, 通过TCSMP获得土茯苓-薏苡仁的关键成分和靶点, 取两者交集, 建立PPI网络并且可视化分析, 核心靶点进行GO和KEGG富集分析, 最终采用分子对接验证成分-靶点之间关系可靠性。结果发现土茯苓-薏苡仁有15个成分及393个靶点, 痛风和高尿酸血症共计660个靶点, 药对治疗疾病的核心成分主要集中在薯蓣皂素、槲皮素、柚皮素等, 关联核心靶点有BCL2、CASP3、MAPK3等。通过干预腺体发育、体液水平调节等涉及膜筏、膜微域多组分、调节核受体活性等。富集分析显示包括EGFR酪氨酸激酶抑制剂耐药、磷脂酶D信号通路、血小板活化等多条通路。利用分子对接, 确定这些核心基因与药物的主要活性成分的结合亲和力。研究深入探讨了土茯苓-薏苡仁对痛风及高尿酸血症的治疗潜力, 并揭示了其可能通过多靶点、多成分、多生物功能和多通路协同影响的作用机制, 展现了中医药在复杂疾病治疗中的独特优势。

关键词: 网络药理学, 分子对接, 土茯苓, 薏苡仁, 痛风, 高尿酸血症

Abstract:

To elucidate the mechanisms underlying the therapeutic effects of the herbal medicine pair Smilax Glabra and Semen Coicis in treating gout and hyperuricemia, a comprehensive analysis was conducted using network pharmacology and molecular docking methods. Disease-associated targets for gout and hyperuricemia were identified from the GeneCards, OMIM, Disgenet, and TTD databases, while the key active components and their corresponding targets for Smilax Glabra and Semen Coicis were obtained from the TCSMP database. The intersection of these targets enabled the construction of a protein-protein interaction (PPI) network, which was subsequently visualized and analyzed. Core targets were further subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to elucidate the biological processes and pathways involved. Molecular docking was then employed to validate the reliability of the interactions between the active components and the identified targets. The analysis revealed that Smilax Glabra and Semen Coicis contained 15 bioactive components that interacted with 393 potential targets, while gout and hyperuricemia were associated with 660 targets in total. The primary active compounds implicated in treating these conditions included diosgenin, quercetin, and naringenin, which were found to interact with crucial hub targets such as BCL2, CASP3, and MAPK3. These interactions suggested that the herbal medicine pair modulated several biological processes, including gland development and the regulation of body fluid levels, through pathways involving membrane rafts, membrane microdomains, and nuclear receptor activities. Enrichment analyses highlighted their involvement in multiple signaling pathways, such as EGFR tyrosine kinase inhibitor resistance, phospholipase D signaling, and platelet activation. Molecular docking confirmed the strong binding affinities between the hub genes and the major active components, supporting their potential role in therapeutic efficacy. This study demonstrated that Smilax Glabra and Semen Coicis might offer a promising therapeutic strategy for gout and hyperuricemia by targeting multiple molecular components, biological functions, and pathways. The findings underscored the unique potential of traditional Chinese medicine (TCM) in managing complex diseases by leveraging synergistic effects across diverse biological mechanisms.

Key words: Network pharmacology, Molecular docking, Smilax Glabra, Semen Coicis, Gout, Hyperuricemia

Supporting:

王毅, 熊玉瑶. 基于网络药理学和分子对接探究土茯苓-薏苡仁药对治疗痛风及高尿酸血症的作用机制[J]. 中国药学(英文版), 2025, 34(8): 741-754.

Yi Wang, Yuyao Xiong. Mechanistic insights into Smilax Glabra and Semen Coicis for gout and hyperuricemia treatment: a network pharmacology and molecular docking approach[J]. Journal of Chinese Pharmaceutical Sciences, 2025, 34(8): 741-754.

图/表 11

Figure 1. Flowchart of the research process.

Table 1. Main active components of Smilax Glabra and Semen Coicis.

Figure 2. Smilax Glabra and Semen Coicis are used to compose the target network diagram.

Figure 3. Disease-drug targets Venn diagram. (A) Venn diagram of therapeutic targets for gout; (B) Venn diagram of therapeutic targets for hyperuricemia; (C) Venn diagram of therapeutic targets for gout and hyperuricemia; (D) Venn diagram of targets for disease (hyperuricemia and gout) and targets for herbs (Smilax Glabra and Semen Coicis).

Figure 4. PPI network diagram. (A) PPI network diagram of genes intersecting with gout and hyperuricemia in Smilax Glabra and Semen Coicis; (B) Visualization of PPI network in Cytoscape.

Table 2. CytoHubba 10-item scoring gene rankings.

Figure 5. Top 10 targets of the CytoHubba 10-item algorithm. Note: The red-highlighted genes appear at least five times.

Figure 6. GO enrichment analysis of Smilax Glabra and Semen Coicis in treatment of gout and hyperuricemia.

Figure 7. KEGG enrichment analysis of Smilax Glabra and Semen Coicis in the treatment of gout and hyperuricemia.

Figure 8. Molecular docking heat map (kcal/mol).

Figure 9. Molecular binding of core components of Smilax Glabra and Semen Coicis to hub gene of gout and hyperuricemia.

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