机器学习和灵敏度分析用于预测针对性沉积的鼻腔药物输送
Hadrien Calmet1, Damien Dosimont1, David Oks2
1Barcelona Super-Computing Centre,(BSC-CNS), Department of Computer Applications in Science and Engineering, Barcelona, Spain.
International journal of pharmaceutics
|June 15, 2023
概括
颗粒大小会影响嗅觉区域的鼻腔药物沉积,而喷雾角度会影响前部沉积. 机器学习准确地预测了沉积物,尽管数据集很小.
科学领域:
- 药理动力学和药物输送方法
- 计算流体动力学的流体动力学.
- 生物医学工程 生物医学工程
背景情况:
- 向性鼻腔药物输送提供了增强的疗效,但受药物输送技术和设备参数的影响.
- 优化鼻腔药物输送需要理解众多变量之间的复杂相互作用.
- 计算建模对于探索影响粒子沉积的广参数空间至关重要.
研究的目的:
- 研究各种喷雾参数对不同鼻腔区域药物颗粒沉积的影响.
- 评估机器学习模型在预测鼻腔药物沉积方面的有效性.
- 确定影响鼻腔药物输送效率的关键参数.
主要方法:
- 通过改变六个输入参数 (例如喷雾角度,速度,颗粒大小) 和三个吸入流量,模拟了384个独特的喷雾特性组合.
- 使用时间平均结流场方法与大模拟来降低计算成本.
- 追踪粒子轨迹以确定前,中,嗅觉和后鼻区域的沉积.
主要成果:
- 颗粒大小分布是嗅觉和后鼻区域沉积的重要因素.
- 喷雾装置插入角度极大地影响了前鼻和中鼻区域的沉积.
- 机器学习模型证明了对粒子沉积的准确预测能力,即使使用有限的数据集.
结论:
- 喷雾特性,特别是颗粒大小和插入角度,在鼻腔药物沉积模式中起着至关重要的作用.
- 计算机模拟与机器学习相结合,为优化鼻腔药物输送系统提供了一种可行的方法.
- 进一步的研究可以利用这些发现来设计更有效的鼻腔药物配方和输送装置.
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