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在连续湿颗粒和干燥中使用直角PAT方法的LOD预测模型的证明

Katharina Kiricenko¹, Stefan Klinken¹, Peter Kleinebudde¹

¹Heinrich Heine University Düsseldorf, Faculty of Mathematics and Natural Science, Institute of Pharmaceutics and Biopharmaceutics, Universitätsstraße 1, 40225 Düsseldorf, Germany.

Journal of pharmaceutical sciences

|July 14, 2024

概括

在干燥过程中实时监测剩余水含量对于连续制造至关重要. 这项研究展示了一种直角过程分析技术 (PAT) 方法,使用近红外 (NIR) 光谱和质量平衡 (MB) 来准确预测干燥损失 (LOD).

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.
频谱学是一种光谱学.

背景情况:

在连续制造中,对关键质量属性的实时监控,如残留水含量 (干燥损失,LOD) 是至关重要的.
近红外 (NIR) 谱系是一种关键的过程分析技术 (PAT),用于在颗粒和干燥过程中在线监测LOD.

研究的目的:

根据过程参数开发和验证LOD的预测模型.
为了证明在振动流化床干燥器 (VFBD) 中用于LOD监测的NIR和质量平衡 (MB) 结合的正交PAT方法的有效性.

主要方法:

开发一个具有成本效益的,内部的NIR传感器用于LOD测量.
应用一个整合NIR和MB的正交PAT策略,用于实时LOD确定.
根据标准红外干燥方法进行验证.

主要成果:

尼尔射线传感器与标准LOD方法的相关性很好.
结合NIR和MB方法的准确性很高 (Pearson r > 0.99对于LOD高达16% w/w).
预测模型实现了LOD预测的R2为0.882和RMSE为0.475.

结论:

使用NIR和MB的正交PAT方法提高了VFBD中LOD监控的可靠性.
这种综合方法减轻了与单一技术故障相关的风险,确保了流程控制.
开发的模型有效地预测了LOD,支持高效的连续制造工艺.

关键词:

连续制造连续制造制造预测 LOD 的预测质量平衡的质量平衡.在NIR光谱学中使用NIR光谱.这就是PATAT PATAT.振动式液化床干燥机床干燥机

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