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无校准光声断层成像:利用多向平行因子分析模型在筛选中定位选择性结晶或沉淀的最佳点

Calibration-free PAT: Locating selective crystallization or precipitation sweet spot in screenings with multi-way PARAFAC models.

作者信息

Wegner Christina Henriette, Hubbuch Jürgen

机构信息

Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.

出版信息

Front Bioeng Biotechnol. 2022 Dec 14;10:1051129. doi: 10.3389/fbioe.2022.1051129. eCollection 2022.

Abstract

When developping selective crystallization or precipitation processes, biopharmaceutical modalities require empirical screenings and analytics tailored to the specific needs of the target molecule. The multi-way chemometric approach called parallel factor analysis (PARAFAC) coupled with ultraviolet visible light (UV/Vis) spectroscopy is able to predict specific concentrations and spectra from highly structured data sets without the need for calibration samples and reference analytics. These calculated models can provide exploratory information on pure species spectra and concentrations in all analyzed samples by representing one model component with one species. In this work, protein mixtures, monoclonal antibodies, and virus-like particles in chemically defined and complex solutions were investigated in three high-throughput crystallization or precipitation screenings with the aim to construct one PARAFAC model per case. Spectroscopic data sets of samples after the selective crystallization or precipitation, washing, and redissolution were recorded and arranged into a four-dimensional data set per case study. Different reference analytics and pure species spectra served as validation. Appropriate spectral preprocessing parameters were found for all case studies allowing even the application of this approach to the third case study in which quantitative concentration analytics are missing. Regardless of the modality or the number of species present in complex solutions, all models were able to estimate the specific concentration and find the optimal process condition regarding yield and product purity. It was shown that in complex solutions, species demonstrating similar phase behavior can be clustered as one component and described in the model. PARAFAC as a calibration-free approach coupled with UV/Vis spectroscopy provides a fast overview of species present in complex solution and of their concentration during selective crystallization or precipitation, washing, and redissolution.

摘要

在开发选择性结晶或沉淀工艺时,生物制药制剂需要根据目标分子的特定需求进行经验筛选和分析。称为平行因子分析(PARAFAC)的多元化学计量学方法与紫外可见光谱(UV/Vis)相结合,能够从高度结构化的数据集中预测特定浓度和光谱,而无需校准样品和参考分析。这些计算模型可以通过用一种物质表示一个模型成分,提供所有分析样品中纯物质光谱和浓度的探索性信息。在这项工作中,对化学定义的和复杂溶液中的蛋白质混合物、单克隆抗体和病毒样颗粒进行了三次高通量结晶或沉淀筛选,目的是每种情况构建一个PARAFAC模型。记录了选择性结晶或沉淀、洗涤和再溶解后样品的光谱数据集,并针对每个案例研究整理成一个四维数据集。不同的参考分析和纯物质光谱用作验证。为所有案例研究找到了合适的光谱预处理参数,甚至允许将该方法应用于缺少定量浓度分析的第三个案例研究。无论复杂溶液中的制剂类型或物质数量如何,所有模型都能够估计特定浓度,并找到关于产率和产品纯度的最佳工艺条件。结果表明,在复杂溶液中,表现出相似相行为的物质可以聚为一个成分并在模型中进行描述。PARAFAC作为一种无需校准的方法与UV/Vis光谱相结合,能够快速概述复杂溶液中存在的物质及其在选择性结晶或沉淀、洗涤和再溶解过程中的浓度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b8f/9797130/92df8960fa0c/fbioe-10-1051129-g001.jpg

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