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采用经验目标分布优化(ETDO)的药物粉末近红外分析。

Near infrared analysis of pharmaceutical powders with empirical target distribution optimization (ETDO).

机构信息

Novo Nordisk A/S, Oral Analytical Development, Novo Nordisk Park, Måløv, Denmark.

Department of Pharmacy, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.

出版信息

J Pharm Biomed Anal. 2020 Mar 20;181:113059. doi: 10.1016/j.jpba.2019.113059. Epub 2019 Dec 20.

DOI:10.1016/j.jpba.2019.113059
PMID:31978645
Abstract

Near infrared (NIR) spectroscopy is a well-established method for analysis of pharmaceutical products, and especially useful for process monitoring and control of continuous production due to high sample throughput. In this work, a previously established method called empirical target distribution optimization (ETDO) wherein reference sample values using information from model prediction of the calibration data was used as a tool to improve the performance of NIR partial least squares (PLS) models. Model performance was assessed using root mean square error (R), bias and accuracy in prediction of test samples. A target value selection threshold was tested to assess the ETDO procedure for NIR analysis of powder samples. The amount of specific variation captured by the model was examined and compared for models calibrated with and without ETDO. The results reported in this work suggests that PLS models optimized with ETDO of reference values can provide more specific PLS models for NIR analysis for complex powder mixtures. In addition, the model optimization method could also be applied as a tool to verify the necessary amount of PLS components to produce robust models. The ETDO method presented in this work is an approach that could be applied in the development of continuous blending or tableting processes where robust in-line quantitative analysis of powder samples is needed.

摘要

近红外(NIR)光谱是一种成熟的药物分析方法,由于其具有较高的样品通量,因此特别适用于连续生产过程的监测和控制。在这项工作中,我们使用了一种称为经验目标分布优化(ETDO)的已有方法,该方法利用校准数据的模型预测信息来使用参考样品值作为工具,以提高 NIR 偏最小二乘(PLS)模型的性能。通过对测试样品的预测来评估模型的均方根误差(R)、偏差和准确性,以评估模型性能。我们还测试了目标值选择阈值,以评估 NIR 粉末样品分析的 ETDO 程序。我们还检查并比较了用和不用 ETDO 进行校准的模型所捕获的特定变化量。本工作的结果表明,通过 ETDO 对参考值进行优化的 PLS 模型可以为复杂粉末混合物的 NIR 分析提供更具体的 PLS 模型。此外,该模型优化方法还可以用作验证产生稳健模型所需的 PLS 组件数量的工具。本工作中提出的 ETDO 方法是一种可应用于连续混合或压片过程开发的方法,在这些过程中需要对粉末样品进行稳健的在线定量分析。

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