基于NIR光谱和集体学习的药物颗粒芯的物理性质的快速表征
Sijun Wu1, Chaoliang Jia1, Li Wang1
1College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
概括
近红外 (NIR) 光谱学与集体学习相结合,提供了一种快速的方法来评估颗粒核心的物理性质. 包装-GRNN模型证明了药物释放特征的出色预测准确性.
科学领域:
- 制药科学 制药科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 颗粒芯的物理特性显著影响持续释放配方中的药物释放.
- 对这些物理性质的准确和快速评估对于制药制造中的质量控制至关重要.
- 物理财产评估的传统方法可能是耗时和劳动密集的.
研究的目的:
- 开发和验证一种快速的方法来评估颗粒芯的物理性质,使用近红外 (NIR) 光谱和集体学习.
- 为了比较不同化学测量算法 (DS,PLSR,GRNN) 的性能,这些算法与引导集成 (Bagging) 进行了增强.
- 建立一个简化但准确的例行质量控制模型.
主要方法:
- 采用近红外光谱 (NIR) 来从颗粒芯中获取光谱数据.
- 研究了三个算法:直接标准化 (DS),部分最小平方回归 (PLSR) 和通用回归神经网络 (GRNN).
- 应用Bootstrap聚合 (Bagging) 整体学习来增强个别算法的预测性能.
主要成果:
- 在测试的组合方法中,Bagging-GRNN模型表现出最高的预测能力.
- 开发的模型实现了高精度,大多数物理参数 (除了粒子间孔隙性) 的平均绝对偏差低于1.0.
- 一个使用60个关键变量的简化Bagging-GRNN模型,对静止角度,粒子大小 (D50) 和圆度等关键参数保持了令人满意的预测性能.
结论:
- 结合NIR光谱和整体建模,特别是Bagging-GRNN,提供了一种强大而快速的方法来表征颗粒核心的物理性质.
- 这种方法为传统技术提供了有希望的替代方案,使药物开发过程中能够有效地进行质量控制.
- 开发的战略促进了快速和准确的评估,有助于优化持续释放药物配方.
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