在使用表面粗性特征的arformoterol-lactose干粉吸入器配方中,基于机器学习的空气动力学性能预测
Jin-Hyuk Jeong1, Soyeon Hong2, Ji-Hyeon Kwon1
1College of Pharmacy, Chungbuk National University, Cheongju 28160, Republic of Korea.
International journal of pharmaceutics
|September 30, 2025
概括
机器学习模型使用SEM图像的表面粗度来预测干粉吸入器 (DPI) 的空气动力性能. 关键参数,如和斜度优化吸入式干粉配方设计,用于肺药物输送.
科学领域:
- 制药科学 制药科学
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
背景情况:
- 干粉吸入器 (DPI) 对于肺部药物输送至关重要.
- 粒子表面形态显著影响DPI空气动力学性能.
- 需要从表面特征中对空气动力学行为的定量预测.
研究的目的:
- 开发一个机器学习框架来预测DPI空气动力学行为.
- 为了将SEM图像中的表面粗度参数与空气动力学性能指标相关联.
- 确定用于优化DPI配方设计的关键形态指标.
主要方法:
- 准备了十种DPI配方,储存和成分各异.
- 使用ImageJ和SurfCharJ提取了13个表面粗度特征.
- 训练了各种机器学习模型 (MLP,SVM,LR,CNN) 来预测发射剂量 (ED),输送剂量 (DD),细颗粒分数 (FPF) 和细颗粒剂量 (FPD).
主要成果:
- 具有射频特征选择的MLP最好预测ED和DD.
- 在SEM图像上训练的CNN显示FPF和FPD的优异预测.
- 表面粗度参数kurtosis (Rku) 和斜度 (Rsk) 是关键预测指标,证明了非线性关系并确定了屈曲值.
结论:
- 较高的Rku和Rsk值与更好的分散性能 (更高的DD,FPF,FPD) 和高效的分聚相关联.
- 这种综合性方法为合理的DPI配方设计提供了数据驱动的基础.
- Rku和Rsk是优化吸入式干粉在肺部药物输送中的性能的实际形态指标.
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