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Updated: May 2, 2026

Coherent anti-Stokes Raman Scattering CARS Microscopy Visualizes Pharmaceutical Tablets During Dissolution

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Published on: July 4, 2014

在连续粉末混合过程中实时释放试验,通过NIR光谱和机器视觉测试以实时测试试验室溶解和混合物均性

Lilla Alexandra Mészáros¹, Martin Gyürkés¹, Emese Varga¹

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.

European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V

|June 16, 2024

概括

本研究引入了第一个基于人工智能的实时模型,用于在连续制药生产过程中监测药品质量. 开发的系统有效地预测了乙酸的溶解,确保了产品的一致性.

科学领域:

制药制造业制药制造业制药制造业
过程分析技术 (PAT) 是一种分析技术.
人工智能在药物开发中的作用

背景情况:

连续制造需要实时,非破坏性质量监测.
已经探索了替代体外溶解测试,但很少在实时中证明.
原材料属性的变化,如晶体大小,可以影响最终药物产品的性能.

研究的目的:

开发和展示基于人工智能的溶解替代品模型的在线适用性,用于连续制造.
实时评估乙酸 (ASA) - 微晶纤维素 (MCC) 混合物的混合均性和溶解.
为了研究不同ASA晶体大小对产品溶解在连续过程中的影响.

主要方法:

开发基于近红外 (NIR) 光谱的模型,使用部分最小平方回归和人工神经网络.
在连续混合过程中对人工智能溶解替代模型进行线上和线下测试.
使用基于机器视觉的粒子大小分析和与测量溶解曲线 (f2 = 80.5) 的比较来验证模型预测.

主要成果:

在线监测系统成功检测到ASA含量和溶解的变化.
人工智能模型准确地预测了溶解概况,与实验数据相对应.
机器视觉分析证实了粒子大小对溶解的影响,验证了模型的发现.

结论:

开发的基于人工智能的溶解替代品模型适用于实时,在线质量监测在连续制药制造.
这种方法可以检测工艺变化,并确保产品质量的一致性.
该研究推进了实时释放测试 (RTRT) 在端到端连续制造过程中的实施.

关键词:

连续的混合混合.溶解预测溶解预测终端到终端的制造业机器视觉机器视觉机器视觉过程分析技术过程分析技术实时释放测试实时释放测试

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