中国药物警戒 ›› 2026, Vol. 23 ›› Issue (3): 250-258.
DOI: 10.19803/j.1672-8629.20250690

• 基础与临床研究 • 上一篇    下一篇

软枣猕猴桃抗氧化关键活性成分及潜在作用机制研究

王若谨1, 孙阳2△, 孙琦1, 巩丽颖3, 刘钊2, 赵楠1, 岳佳音1,4, 梁德军2#, 孟繁浩1,*   

  1. 1中国医科大学药学院,辽宁 沈阳 110122;
    2辽宁省经济林研究所,辽宁 大连 116031;
    3辽宁省人民医院检验科,辽宁 沈阳 110016;
    4沈阳医学院药学院,辽宁 沈阳 110034
  • 收稿日期:2025-09-29 出版日期:2026-03-15 发布日期:2026-03-17
  • 通讯作者: *孟繁浩,男,博士,教授,药物作用机制研究。E-mail: fhmeng@cmu.edu.cn。#为共同通信作者。
  • 作者简介:王若谨,女,硕士,中药活性成分研究。为并列第一作者。
  • 基金资助:
    国家自然科学基金资助项目(32400329)

Key Active Components and Potential Mechanisms of Antioxidant Effects of Actinidia arguta

WANG Ruojin1, SUN Yang2△, SUN Qi1, GONG Liying3, LIU Zhao2, ZHAO Nan1, YUE Jiayin1,4, LIANG Dejun2#, MENG Fanhao1,*   

  1. 1School of Pharmacy, China Medical University, Shenyang Liaoning 110122, China;
    2Liaoning Institute of Economic Forestry, Dalian Liaoning 116031, China;
    3Clinical Laboratory, the People’s Hospital of Liaoning Province, Shenyang Liaoning 110016, China;
    4School of Pharmacy, Shenyang Medical University, Shenyang Liaoning 110034, China
  • Received:2025-09-29 Online:2026-03-15 Published:2026-03-17

摘要: 目的 基于网络药理学预测软枣猕猴桃抗氧化的关键活性成分、核心靶点及信号通路,并通过体外实验对主要成分的抗氧化活性进行验证。方法 通过HERB数据库和TCMSP数据库检索并筛选软枣猕猴桃的活性成分及其对应靶点,构建“药物-成分-靶点图”;从疾病数据库获得抗氧化相关靶点,与成分靶点取交集后,对交集靶点进行蛋白质-蛋白质相互作用(PPI)网络分析、GO功能富集分析和KEGG通路富集分析。采用DPPH与ABTS+自由基清除实验,对预测的关键活性成分进行体外抗氧化活性验证。结果 共筛选出软枣猕猴桃的8种活性成分,对应283个作用靶点,与抗氧化靶点取交集获得118个共同靶点。PPI分析显示核心靶点包括STAT3、SRC、PARP1、MTOR、MMP9等。GO功能富集分析得到生物过程条目571条、细胞组分条目81条、分子功能条目190条。KEGG通路富集分析筛选出前20条显著通路,主要包括癌症通路、化学致癌-活性氧、细胞信号传导、脂质与动脉粥样硬化等。体外抗氧化活性实验证实,预测成分均具有抗氧化能力,其中槲皮素、咖啡酸及3,4-二羟基苯甲酸的活性最强。结论 软枣猕猴桃中的关键活性成分,如3,4-二羟基苯甲酸、猕猴桃碱、咖啡酸、4-羟基苯甲酸、小檗碱、槲皮素等均具有较高的自由基清除能力,可能通过癌症通路、化学致癌作用-活性氧、细胞信号传导通路、脂质与动脉粥样硬化等信号通路,作用于STAT3、SRC、PARP1、MTOR、MMP9等靶点,从而发挥抗氧化作用。

关键词: 软枣猕猴桃, 抗氧化, 活性成分, 作用机制, 靶点, 网络药理学, 体外实验

Abstract: Objective To investigate the key bioactive components, target proteins, and signaling pathways involved in the antioxidant effects of Actinidia arguta based on network pharmacology and to verify the antioxidant activity of these components via in vitro experiments. Methods A “drug-ingredient-target map” was constructed by searching the HERB and TCMSP databases and screening the active ingredients and corresponding targets of Actinidia arguta. Protein-protein interaction (PPI) network analysis of intersection targets, GO functional enrichment analysis, and KEGG pathway enrichment analysis were conducted after antioxidant-related targets were obtained from disease databases and intersected with component targets. The in vitro antioxidant activity of the key active ingredients was validated via DPPH and ABTS+ radical scavenging experiments. Results By searching the HERB and TCMSP databases, eight active components and 283 corresponding targets were identified. Antioxidant-related targets were obtained from disease databases, and 118 common targets were identified by taking the intersection of these datasets. Protein-protein interaction network analysis revealed such core targets as STAT3, SRC, PARP1, MTOR, and MMP9. GO functional enrichment analysis suggested that these targets were mostly involved in biological processes, cellular components, and molecular functions. KEGG pathway enrichment analysis found that the antioxidation mechanisms might be closely related to such pathways as cancer, chemical carcinogenesis-reactive oxygen species, cellular signal transduction, and lipid and atherosclerosis pathways. Conclusion The in vitro antioxidation experiments have confirmed that all the predicted components are capable of antioxidation, with quercetin, caffeic acid, and 3,4-dihydroxybenzoic acid as the strongest ones. Such key components as 3,4-dihydroxybenzoic acid, actinidine, and caffeic acid have a strong ability to scavenge free radicals. They may exert antioxidant effects through multi-target and multi-pathway mechanisms, which is expected to shed light on the antioxidation mechanism of Actinidia arguta.

Key words: Actinidia arguta, Antioxidant, Active Components, Mechanism of Action, Targets, Network Pharmacology, in vitro Experiments

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