安妮辅助紫外线成像用于HPMC矩阵片的非破坏性溶解预测
Orsolya Péterfi1, Lilla Alexandra Mészáros1, Bence Szabó-Szőcs1
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
|February 7, 2026
概括
紫外线成像与人工神经网络 (ANN) 结合,通过分析基基甲基纤维素 (HPMC) 含量,准确地预测延长释放片中的药物溶解. 这种非破坏性方法可以快速评估配方变化.
科学领域:
- 制药科学 制药科学
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
背景情况:
- 延长释放药物递送系统需要精确控制药物释放动力学.
- 基甲基纤维素 (HPMC) 是矩阵片中的关键辅助剂,影响药物释放率.
- 监测药物释放的传统方法可能耗时且具有破坏性.
研究的目的:
- 开发和验证一种用于估计HPMC矩阵片中药物溶解行为的非破坏性方法.
- 调查紫外线成像和人工神经网络 (ANN) 的实时监控配方变化的实用性.
- 为了将紫外线图像中的光学信息与基甲基纤维素 (HPMC) 含量和溶解概况相关联.
主要方法:
- 制备了延长释放片的配方,其HPMC含量各不相同 (5-35%) 和咖啡因作为模型药物.
- 使用紫外线成像来捕获从平板电脑中的光学信息.
- 人工神经网络 (ANN) 使用紫外线图像的色度数据进行训练,以预测溶解.
- 在动态条件下的外部验证和测试集上使用f2相似度因子评估模型性能.
主要成果:
- 使用RGB颜色空间蓝色通道的ANN模型实现了高平均f2相似度系数80.68,用于预测溶解曲线.
- 经过训练的模型成功地反映了药片成分的动态变化,并预测了药物释放行为的相应变化.
- 紫外线成像为准确的溶解建模提供了宝贵的,聚合物特定的光学数据.
结论:
- 紫外线成像与ANN相结合,提供了一种快速,非破坏性的方法来评估HPMC矩阵片中的药物释放.
- 这种方法可以监测辅助剂水平的配方变化及其对药物释放的影响.
- 这些发现扩大了基于成像技术在制药开发和质量控制中的应用.
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