摘要
目的 研究潞党参化学成分及其抗炎活性.方法 潞党参 70％乙醇提取物采用硅胶、ODS、制备HPLC进行分离纯化,根据理化性质及波谱数据鉴定所得化合物的结构.采用RAW264.7 模型评价其体外抗炎活性.结果 从中分离得到16 个化合物,分别鉴定为ethylsyringin(1)、7-O-ethyltangshenoside Ⅱ(2)、triandrin(3)、反式异松柏苷(4)、甲基丁香苷(5)、9-acetoxy syringin(6)、鬼针聚炔苷 B(7)、codonopiloenynenoside A(8)、codonopilodiynoside F(9)、铜锤玉带草炔苷 B(10)、党参炔苷(11)、落叶松脂醇-4-O-β-D-葡萄糖苷(12)、dihydrodehydrodiconiferyl alcohol 4′-O-β-D-glucoside(13)、苍术内酯Ⅲ(14)、白曼陀罗酰胺(15)、苯甲醇-7-O-α-L-吡喃阿拉伯糖-(1→ 6)-β-D-吡喃葡萄糖苷(16).化合物 1～2、5、7～11、13～15 具有一定的抗炎活性,其中 11、14～15 活性较强,IC50值分别为(18.23±4.18)、(17.73±3.12)、(14.89±2.47)μmol/L.结论 化合物 3、6、13、16 为首次从桔梗科植物中分离得到,2、5、15 为首次从该植物中发现.化合物 11、14、15 具有良好的抗炎活性.
Abstract
AIM To study the chemical constituents from Codonopsis pilosula(Franch.)Nannf in Shanxi and their anti-inflammatory activities.METHODS The 70％ ethanol extract from C.pilosula in Shanxi was isolated and purified by silica gel,ODS and preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.Their in vitro anti-inflammatory activities were evaluated by RAW264.7 model.RESULTS Sixteen compounds were isolated and identified as ethylsyringin(1),7-O-ethyltangshenoside Ⅱ(2),triandrin(3),trans-isoconiferin(4),methylsyringin(5),9-acetoxy syringin(6),cordifolioidyne B(7),codonopiloenynenoside A(8),codonopilodiynoside F(9),pratialin B(10),lobetyolinin(11),lariciresinol-4-O-β-D-glucoside(12),dihydrodehydrodiconiferyl alcohol 4′-O-β-D-glucoside(13),atractylenolid Ⅲ(14),baimantuoluoamide B(15),benzyl primeveroside(16).Compounds 1-2,5,7-11 and 13-15 had certain anti-inflammatory activities,among which compounds 11,14-15 had higher activities,whose IC50 values were(18.23±4.18),(17.73±3.12),(14.89±2.47)μmol/L,respectively.CONCLUSION Compounds 3,6,13,16 are first isolated from Campanulaceae,2,5,15 are first found from this plant.Compounds 11,14 and 15 have good anti-inflammatory activities.
基金项目
山西省教育厅科技创新计划(2021L359)
山西省科技创新人才团队专项计划(202204051002028)
山西省卫生健康委员会重点实验室建设计划(2021SYS23)
山西中医药大学博士科研启动基金(2022BK14)
山西中医药大学科技创新团队(2022TD1015)
山西中医药大学学科建设(2024XKJS-26)
国家科技期刊平台
NSTL
万方数据