合理化鼻至脑药物输送:以机器学习为指导,优化和机械阐明鼻喷雾的嗅觉沉积
Zizhao Zhai1, Wenhao Wang2, Guanlin Wang2
1State Key Laboratory of Bioactive Molecules and Druggability Assessment, Guangdong Basic Research Center of Excellence for Natural Bioactive Molecules and Discovery of Innovative Drugs, Jinan University, Guangzhou 510632, Guangdong, PR China; College of Pharmacy, Jinan University, Guangzhou 510632, Guangdong, PR China.
概括
使用机器学习优化固体脂质纳米粒子鼻喷雾 (SLN-NS) 显著提高了嗅觉沉积. 这提高了神经系统疾病,如阿尔茨海默氏症和帕金森病的药物输送.
科学领域:
- 药理学 药理学是指药理学的学科.
- 生物技术是生物技术.
- 材料科学 材料科学 材料科学
背景情况:
- 鼻子到大脑的药物输送受到鼻喷雾中嗅觉沉积不佳的阻碍.
- 固体脂质纳米粒子装载的鼻喷雾剂 (SLN-NS) 提供了增强中枢神经系统 (CNS) 药物递送的潜力.
- 优化配方对于克服解剖障碍和改善药物向至关重要.
研究的目的:
- 通过机器学习和高通量实验的结合,系统地优化SLN-NS的嗅觉沉积.
- 建立分散介质特性,喷雾性能和嗅觉沉积之间的相关性.
- 开发一种改进的SLN-NS配方,以增强中枢神经系统的药物输送.
主要方法:
- 设计了40种SLN-NS配方的分散介质特性,使用了五种辅助剂,度不同.
- 研究了物理化学性质,风质参数,喷雾性能和嗅觉沉积之间的相关性.
- 开发并验证了一种随机森林机器学习模型,以预测嗅觉沉积.
主要成果:
- 建立了一个三级的相关性,将分散的介质特性,喷雾性能和嗅觉沉积联系起来.
- 证明了基本的物理化学性质影响滴滴特性,而风湿学参数影响喷雾形态.
- 通过优化配方实现了19.77%的嗅觉沉积,比传统喷雾剂提高了4-20倍.
- 在偏头痛,阿尔茨海默氏症和帕金森病的动物模型中证实了增强的大脑输送和治疗疗效.
结论:
- 机器学习和高通量实验为优化鼻喷雾配方提供了一个合理的框架.
- 优化的SLN-NS配方显著增强嗅觉沉积和大脑药物输送.
- 这种方法有望改善中枢神经系统疾病的治疗结果.
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