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Monitoring multiple quality attributes of a complex Fc-fusion protein during cell culture production processes by mD-LC-MS peptide mapping

化学 糖基化 去酰胺 色谱法 质谱法 岩藻糖基化 串联质谱法 单同位素质量 胰蛋白酶 生物化学 聚糖 糖蛋白
作者
Julien Camperi,Sanket Dahotre,Davy Guillarme,Cinzia Stella
出处
期刊:Talanta [Elsevier]
卷期号:246: 123519-123519 被引量:15
标识
DOI:10.1016/j.talanta.2022.123519
摘要

Fc-fusion proteins represent a successful class of biopharmaceutical products. They are considered highly heterogeneous products due to the common degradation of amino acids that occurs during their production in upstream and downstream processes (e.g., oxidation and deamidation) and, above all, their complex glycosylation profile. Multi-dimensional liquid chromatography-mass spectrometry (mD-LC-MS) has recently gained much interest for process analytical technology, enabling the integration of this analytical technology in production and purification environments. In this study, an online mD-LC-MS/MS peptide mapping method was developed for monitoring multiple quality attributes, including the N-glycosylation state of a complex Fc-fusion protein, which is made by combining two heavily glycosylated cytokines with an Fc domain. This fully automated workflow includes sample purification, reduction, digestion, peptide mapping, and subsequent mass spectrometric analysis. Two immobilized enzyme cartridges based on trypsin and Lys-C protease were employed to generate a detailed glycosylation mapping, as trypsin allowed the identification of only one of four glycosylation sites, while Lys-C was more informative for two other sites. Site-specific glycosylation information such as antennarity, sialyation, and core fucosylation state was also determined. In addition to glycans, other post-translational modifications could be monitored simultaneously during the cell culture production processes by the mD-LC-MS/MS approach. In summary, the generated data demonstrate the applicability of mD-LC-MS for the monitoring and trending of multiple attributes for complex antibody formats over production processes in an automated and fast manner, compared to the complex and time-consuming traditional offline assays.
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