白色咖啡因片的UV/VIS成像用于预测CQAs:API含量,粉碎强度,易碎性,分解时间和溶解概况
Lilla Alexandra Mészáros1, Lajos Madarász1, Máté Ficzere1
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
|August 8, 2024
概括
紫外线/VIS成像有效评估药片质量,预测活性药物成分 (API) 含量,粉碎强度和溶解概况. 这种机器视觉方法为固体剂型提供实用,实时的质量控制.
科学领域:
- 制药技术 制药技术 制药技术
- 分析化学 分析化学
- 机器视觉 机器视觉 机器视觉
背景情况:
- 传统的平板电脑质量控制方法可能耗时且具有破坏性.
- 需要快速,非破坏性的技术来同时评估多个平板电脑质量属性.
- 紫外线/VIS成像为实时制药分析提供了一个潜在的解决方案.
研究的目的:
- 展示UV/VIS成像的应用,以评估白色片的关键质量属性.
- 使用机器视觉来评估API内容的预测准确性,粉碎强度,易碎性,分解时间和溶解配置文件.
- 突出机器视觉在固体剂型制造中的实时质量控制的可行性.
主要方法:
- 制造了具有不同压缩力和无水咖啡因含量的平板电脑.
- 在平板的两侧进行了UV/VIS成像.
- 紫外线图像被用来通过颜色直方图来预测API内容.
- 使用VIS图像进行纹理分析,以预测压力.
- 折性是通过体重减轻来评估的,并且分解时间被分类.
- 机器学习算法和人工神经网络用于分类和溶解配置预测.
主要成果:
- 使用紫外线图像颜色组图,预测API含量的相对误差为5.6%.
- 使用VIS图像纹理分析,预测粉碎强度的相对误差低于10%.
- 药片成功地被分为可碎性和分解时间 (<10s,11-35s,>36s) 的组.
- 使用人工神经网络预测了即时释放溶解配置文件,根平均平方误差 (RMSE) 低于5%.
- 使用了短50毫秒的图像采集时间.
结论:
- 紫外线/VIS成像是一种实用和有效的工具,用于平板电脑的非破坏性质量评估.
- 机器视觉,利用UV/VIS成像和机器学习,可以准确预测关键的平板电脑质量属性.
- 这项技术在制药制造中促进了实时质量控制,无论API颜色如何.
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