波长角映射器用于变量选择的应用在代优化技术中,对制药粉末混合物中药物含量的预测
Adam J Rish1, Samuel R Henson1, Natasha L Velez-Silva1
1Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, USA.
International journal of pharmaceutics
|July 21, 2023
概括
本研究引入了新的无校准波长选择方法,WAM和SWAM,用于近红外 (NIR) 光谱在制药制造. 这些技术通过改善活性药物成分 (API) 监测而提高工艺分析技术 (PAT),而无需广泛的校准数据.
科学领域:
- 制药制造业 制药制造业 制药制造业
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 过程分析技术 (PAT) 对于制药质量控制至关重要.
- 近红外 (NIR) 光谱是监测活性药物成分 (API) 度的关键PAT技术之一.
- 对于NIR光谱解释,需要多变量模型,像代优化技术 (IOT) 这样的无校准方法正在引起人们的兴趣.
研究的目的:
- 开发和评估用于NIR光谱的真正无校准波长选择方法.
- 提高物联网算法的性能和稳定性,用于制药应用.
- 为了减少与传统校准模型相关的材料和时间负担.
主要方法:
- 提出了一种使用波长角度映射器 (WAM) 的无校准波长选择方法.
- 开发了一个扩展,SWAM,使用光谱窗口进行波长选择.
- 将WAM和SWAM性能与依赖校准的模型和用于粉末混合物API监控的基础IOT进行比较.
主要成果:
- WAM为波长选择提供了一种真正的无校准方法.
- 虽然SWAM需要最少的培训数据,但它也表现出了有效性.
- 无论是WAM还是SWAM,其预测性能都与药物混合物中API度的现有方法相提并论.
结论:
- 开发的WAM和SWAM方法为基于NIR的PAT提供了有效的无校准波长选择.
- 这些方法保持了IOT的优势,同时提高了API监控的预测准确性.
- 提出的技术为制药分析中传统的依赖校准的模型提供了一个有希望的替代方案.
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