基于卷积神经网络的化学图的评估,通过快速拉曼成像来预测平板电脑溶解概况
Dorián László Galata1, Boldizsár Zsiros1, Gábor Knyihár2
1Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary.
International journal of pharmaceutics
|May 31, 2023
概括
快速拉曼成像和卷积神经网络 (CNN) 预测从基甲基纤维素 (HPMC) 平板图像中的药物溶解. 这种方法通过分析HPMC度和颗粒大小,为制造过程提供了洞察力.
科学领域:
- 制药科学 制药科学
- 化学成像技术 化学成像技术
- 机器学习 机器学习
背景情况:
- 基甲基纤维素 (HPMC) 对于持续释放的药片配方至关重要.
- 了解HPMC分布 (度和颗粒大小) 影响药物释放概况.
- 试管溶解的预测模型对于药物开发至关重要.
研究的目的:
- 使用快速拉曼成像来分析平板电脑中的HPMC特征.
- 开发和比较机器学习模型,用于预测体外溶解概况.
- 建立一种用于评估药片质量和制造一致性的新方法.
主要方法:
- 使用快速拉曼成像来捕获HPMC分布的化学图像.
- 卷积神经网络 (CNN) 开发用于图像处理和直接溶解预测.
- 波形分析被用作一个比较方法来量化HPMC纹理.
- 分析了112个培训和32个验证片.
主要成果:
- CNNs准确地预测了HPMC粒子大小及其对溶解的影响 (平均f2相似性=62.95).
- 使用CNN的图像直接溶解预测显示出有希望的结果 (f2 = 54.2).
- CNNs展示了在化学图像中自主识别相关模式的能力.
- 这项研究突出了拉曼成像和CNNs用于制药分析的潜力.
结论:
- 快速拉曼成像与CNN相结合,提供了一个强大的工具来表征平板电脑中的HPMC.
- 这种方法可以准确地预测体外溶解概况.
- 这种方法可以增强对制药制造过程的理解和控制.
相关概念视频
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