鞣花酸 pH 敏感载药微球的释放规律研究
收藏国家林业和草原科学数据中心2022-11-25 更新2024-03-06 收录
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鞣花酸是一种具有抗氧化、抗炎、抗病毒、抗癌等多种生物学效应的多酚类物质,在癌症治疗方面有巨大潜力。但其口服半衰期短,生物利用度低,水溶性差。本文选择明胶/海藻酸钠为基材,制备了具有pH敏感性的鞣花酸明胶/海藻酸钠微球。考察了微球的形态,测定了微球的干燥速率、载药量、包封率和在不同pH介质中的释放规律。结果表明:鞣花酸明胶/海藻酸钠微球呈球形,表面有褶皱;温度越高,干燥速率越快;对浓度为4g/L的鞣花酸分散体系,载药量达到14.36%,包封率为86.16%。在pH2.13水溶液、pH4.02水溶液和蒸馏水(pH=6.86)中24h累计释放率分别为0.98%、2.67%和10.79%,在pH4.00缓冲溶液、pH6.86缓冲溶液和PBS缓冲液(pH=7.4)中24h累计释放率分别为1.35%、21.74%和36.73%,在人工胃液和人工肠液中24h累计释放率则分别为13.92%和90.45%。
Ellagic acid is a polyphenolic compound with multiple biological effects including antioxidant, anti-inflammatory, antiviral and anticancer activities, and holds great potential in cancer therapy. However, it exhibits several drawbacks such as short oral half-life, low bioavailability and poor water solubility. In this study, gelatin/sodium alginate was selected as the base material to prepare pH-responsive ellagic acid-loaded gelatin/sodium alginate microspheres. The morphology of the microspheres was characterized, and the drying rate, drug loading, encapsulation efficiency and release patterns in different pH media were determined. The results showed that the ellagic acid-loaded gelatin/sodium alginate microspheres were spherical with wrinkled surfaces; the higher the temperature, the faster the drying rate. For the ellagic acid dispersion system with a concentration of 4 g/L, the drug loading reached 14.36% and the encapsulation efficiency was 86.16%. The 24-hour cumulative release rates in aqueous solutions at pH 2.13, pH 4.02 and distilled water (pH=6.86) were 0.98%, 2.67% and 10.79% respectively; those in buffer solutions at pH 4.00, pH 6.86 and PBS buffer (pH=7.4) were 1.35%, 21.74% and 36.73% respectively; and those in simulated gastric fluid and simulated intestinal fluid were 13.92% and 90.45% respectively after 24 hours.
提供机构:
国家林业和草原科学数据中心
创建时间:
2022-11-25
搜集汇总
数据集介绍

背景与挑战
背景概述
该数据集聚焦于鞣花酸pH敏感载药微球的释放规律研究,针对鞣花酸口服半衰期短、生物利用度低的问题,通过制备明胶/海藻酸钠微球,考察了其形态、载药量、包封率及在不同pH介质中的释放性能。实验结果表明,微球具有pH敏感性释放特性,例如在人工肠液中24小时累计释放率达90.45%,显著高于酸性环境,为药物缓释应用提供了关键数据支撑。
以上内容由遇见数据集搜集并总结生成



