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使用人工神经网络和响应表面方法制备和优化里瓦罗克萨班固体脂质纳米颗粒

Fatemeh Ghorbannejad Nashli^1,2, Sareh Aghajanpour^1,2, Ali Farmoudeh^1,2

¹Pharmaceutical Sciences Research Center, Hemoglobinopathy Institute, Mazandaran University of Medical Sciences, Sari, Iran.

Journal of microencapsulation

|January 6, 2025

概括

优化的固体脂质纳米颗粒 (SLN) 增强了里瓦洛克萨班的输送,提高了溶解性和生物可用性. 人工神经网络 (ANN) 准确地预测了配方性能,超过了药物输送系统的传统方法.

科学领域:

制药纳米技术的使用
药物输送系统药物输送系统
生物材料科学生物材料科学

背景情况:

作为抗凝剂的里瓦罗克萨班在溶解性和生物可用性方面面临着挑战.
改善药物输送对于治疗疗效和减少副作用至关重要.
固体脂质纳米粒子 (SLN) 为增强药物输送提供了一个有前途的平台.

研究的目的:

为了优化固体脂质纳米颗粒 (SLN) 用于利瓦洛克萨班的输送.
为了达到最小的平均粒子直径和最大的捕获效率 (EE).
为了增强里瓦洛克萨班的溶解性,生物可用性和血脑屏障的透.

主要方法:

使用中央复合设计合成32种SLN配方.
使用响应表面方法 (RSM) 和人工神经网络 (ANN) 进行预测建模.
根据平均直径,捕获效率 (EE),多分散率指数和泽塔潜力来评估配方性能.

主要成果:

优化的SLN实现了159.8nm的平均粒子直径和74.3%的EE.
与RSM相比,ANN模型在预测平均直径和EE方面表现出更高的准确性.
使用SEM,DSC和FTIR分析证实了结构完整性和稳定性.

结论:

优化的SLN配方显示了改善里瓦洛克萨班输送的巨大潜力.
该ANN模型的高精度强调了它们在药物配方优化中的实用性.
这项研究推进了基于SLN的药物输送系统,以提高治疗结果.

关键词:

里瓦罗克萨班 (Rivaroxaban) 是一种药物.中心复合设计设计的中心复合设计.药物输送系统是药物输送系统.神经网络的神经网络的神经网络响应表面方法的方法.固体脂质纳米粒子是一种固体脂质纳米粒子.

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