川西獐牙菜中抗炎活性组分提取纯化工艺研究
Study on the extraction and purification process of the anti-inflammatory components from Swertia mussotii Franch.
毕齐茂 1郝小慧 2刘莉莹 3贾雯靖 3陶吉红 3毕宏涛 3赵晓辉3
作者信息
- 1. 青海师范大学,西宁 810016
- 2. 陕西唐王天洋制药有限公司,汉中 724400
- 3. 中国科学院西北高原生物研究所,西宁 810008
- 折叠
摘要
目的:建立川西獐牙菜中环烯醚萜苷抗炎活性组分快速高效的提取纯化工艺.方法:以獐牙菜苦苷、龙胆苦苷、獐牙菜苷3种环烯醚萜苷为研究对象,采用响应面法和Box-Behnken优化提取工艺参数.筛选大孔树脂柱填料,对环烯醚萜苷组分进行纯化富集,并进行体外抗炎活性研究.结果:最优提取工艺参数为:提取次数2次,提取时间60 min,液料比46 mUg,乙醇浓度73％,提取温度50℃;在该条件下3种环烯醚萜苷的得率为0.47％.筛选得到DM301大孔树脂柱富集纯化环烯醚萜苷类化合物,富集的组分中环烯醚萜苷总含量可达62.30％.结论:建立了川西獐牙菜环烯醚萜苷活性组分的最佳提取和分离纯化工艺,提高了活性组分中环烯醚萜苷含量和药材利用率.
Abstract
Objective:To establish a rapid and efficient extraction and purification process for the anti-inflammatory component of iridoid glycosides from Swertia mussotii Franch..Methods:Single factor experiment,response surface methodology and Box-Behnken were used to optimize the extraction parameters of three iridoid glycosides,swertiamarin,gentiopicroside and sweroside.The macroporous resin column packing was selected,and the iridoid glycosides were purified and enriched,and the anti-inflammatory activity in vitro was studied.Results:The optimal extraction parameters of the three iridoid glycosides from Swertia mussotii Franch.were as follows:extraction times 2 times,extraction time 60 min,liquid-solid ratio 46 mUg,ethanol concentration 73％,extraction temperature 50 ℃.The results showed that the extraction rate of the three iridoid glycosides in Swertia mussotii Franch..was 0.47％.The iridoid glycosides were enriched and purified by DM301 macroporous resin column.The total content of iridoid glycosides in the enriched components was 62.30％.Conclusion:The optimum extraction and purification process of iridoid glycosides from Swertia mussotii Franch.was established,which improved the content of iridoid glycosides and the utilization rate of medicinal materials.
关键词
川西獐牙菜/高效液相色谱/环烯醚萜苷/响应面/工艺优化Key words
Swertia mussotii Franch./HPLC/Iridoid glycosides/Response surface method/Process optimization引用本文复制引用
基金项目
中国科学院重点部署项目(ZDRW-ZS-2020-2-1)
出版年
2024
国家科技期刊平台
NSTL
万方数据