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使用响应表面方法优化Levomilnacipran载荷纳米结构脂质载体

Parthiban Ramalingam¹, Mothilal M¹

¹Department of Pharmaceutics, SRM College of Pharmacy, Kattankulathur, India.

Pharmaceutical nanotechnology

|September 5, 2025

概括

响应表面方法优化了Levomilnacipran纳米结构脂质载体 (LEV- NLC) 的配方. 优化的LEV-NLC显示出有利的颗粒大小,高捕获效率和持续的药物释放,增强治疗潜力.

科学领域:

制药科学
纳米技术
药物输送系统

背景情况:

利沃米尔纳西普兰是一种用于治疗抑郁症的血清 - 上腺素再吸收抑制剂.
纳米结构脂质载体 (NLC) 为改善药物的溶解性,稳定性和受控释放提供了一个有希望的平台.
优化NLC配方对于最大限度地提高治疗效果和患者服药性至关重要.

研究的目的:

优化含有levomilnacipran的纳米结构脂质载体 (LEV-NLC) 的配方.
研究关键配方变量对LEV-NLC特征的影响.
评估优化LEV-NLCs的体外释放特征.

主要方法:

采用了响应表面方法 (RSM) 和中央复合可旋转设计 (CCRD).
研究了三种独立变量:水相与有机相的比率 (X1),药物与脂质的比率 (X2) 和表面活性剂的度 (X3).
测量了粒子大小,泽塔潜力和捕获效率;通过SEM和TEM评估形态;在pH7.4和6.0下研究药物释放.

主要成果:

RSM成功优化了LEV-NLC的配方参数.
最佳配方包括1: 1的水相比和有机相比,1: 7的药物与脂质相比,以及0. 5%的表面活性剂度.
优化LEV-NLC的粒子大小为148nm,zeta电位为36mV,捕获效率为88%,球形形态,持续释放 (pH为7.7%,pH为6.0在24小时内为76%).

结论:

RSM-CCRD是模拟和优化LEV-NLC配方的一个有效工具.
优化的LEV-NLC配方显示了增强Levomilnacipran输送的潜力.
持续释放的特征表明治疗效果有所改善,剂量频率降低.

关键词:

中心复合可旋转设计莱沃米尔纳西普兰纳米结构的脂质载体响应表面的方法分析Zeta潜力.

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