Pieters Sigrid, Roger Jean-Michel, De Beer Thomas, D'Hondt Matthias, De Spiegeleer Bart, Vander Heyden Yvan
Department of Analytical Chemistry and Pharmaceutical Technology, Center for Pharmaceutical Research, Vrije Universiteit Brussel - VUB, Laarbeeklaan 103, Brussels B-1090, Belgium.
Irstea, UMR1201 ITAP, Rue J.F. Breton 361, Montpellier 34191, France.
Anal Chim Acta. 2014 May 12;825:42-50. doi: 10.1016/j.aca.2014.03.027. Epub 2014 Mar 26.
The aim of this work is to build a multivariate calibration (MVC) model from Raman spectra for the prediction of the protein conformational state class (i.e. native-like or non-native) in different freeze-dried pharmaceutical formulations of a model protein lactate dehydrogenase (LDH). As this model would be intended to facilitate and better understand formulation and process development, it should allow acceptable classification performance despite variations in formulation type and batch. Therefore, it was attempted to (1) find which factors interfere the Raman spectra, (2) understand them, and (3) make the MVC model robust for them. A variance analysis within the Raman spectral data space identified significant spectral background variations among certain formulation types and batches in the studied samples. Raw material (i.e. LDH) batch variability and the presence of a Maillard reaction in formulations were the main reasons for this. We demonstrate the successful use of both exhaustive calibration and external parameter orthogonalization (EPO) pre-processing for making the Raman classification model more robust for the expected spectral interferences.
这项工作的目的是基于拉曼光谱构建一个多元校准（MVC）模型，用于预测模型蛋白乳酸脱氢酶（LDH）不同冻干药物制剂中蛋白质的构象状态类别（即天然样或非天然）。由于该模型旨在促进并更好地理解制剂和工艺开发，因此尽管制剂类型和批次存在差异，它也应具备可接受的分类性能。因此，尝试（1）找出干扰拉曼光谱的因素，（2）理解这些因素，以及（3）使MVC模型对这些因素具有稳健性。在拉曼光谱数据空间内进行的方差分析确定了所研究样品中某些制剂类型和批次之间存在显著的光谱背景变化。原材料（即LDH）批次的变异性以及制剂中存在美拉德反应是造成这种情况的主要原因。我们证明了详尽校准和外部参数正交化（EPO）预处理的成功应用，可使拉曼分类模型对预期的光谱干扰更具稳健性。
