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优化纳米脂质体配方:使用机器学习评估影响素载脂质体捕获效率的因素

Benyamin Hoseini¹, Mahmoud Reza Jaafari², Amin Golabpour³

¹Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran.

International journal of pharmaceutics

|September 15, 2023

概括

集体学习优化了黄素载入的脂质体,通过确定理想的配方条件来实现100%的捕获效率 (EE). 这种机器学习方法增强了药物输送系统的开发.

科学领域:

制药科学制药科学
材料科学材料科学材料科学
计算化学计算化学

背景情况:

黄素的生物可用性较差,阻碍了临床使用.
脂质体为黄素的输送提供了一个有前途的解决方案.
优化脂质体配方对于高捕获效率 (EE) 至关重要.

研究的目的:

应用集体学习来优化黄素负载的脂质体配方.
确定影响脂质体EE的关键实验参数.
为了实现高EE和适合的颗粒特征,以有效的药物输送.

主要方法:

使用不同摩尔比率的远程加载来制备的脂质体 (HSPC:DPPG:Chol:DSPE-mPEG2000和HSPC:Chol:DSPE-mPEG2000).
雇佣的最小平方增强 (LSBoost) 组合学习基于摩尔比率,粒子大小,超声波时间,pH和PDI的EE模型.
使用动态光散射来确定粒子大小和PDI;分数据70/30用于训练/测试;使用平均绝对误差 (MAE) 进行评估.

主要成果:

最优的LSBoost模型实现了3.61的低MAE,证明了强大的预测能力.
确定了100%EE的最佳条件,包括55:5:35:5的摩尔比,PDI0.09-0.13,粒子大小~130nm,30分钟的超声波和pH7.2-8.5.
55:40:5的摩尔比率产生了最大EE的88.38%,强调了配方成分的重要性.

结论:

合体学习准确地预测和优化 curcumin-loaded 脂质体的 EE.
开发的模型为有效的药物输送系统设计提供了有价值的见解.
这种机器学习方法加速了脂质体配方的优化过程.

关键词:

库尔库明是一种黄素.药物输送系统是药物输送系统.捕获效率的效率捕获效率脂质体是一种脂质体.机器学习是机器学习.优化优化优化优化

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