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Analytical and bioanalytical chemistry

LC-MS analysis combined with principal component analysis and soft independent modelling by class analogy for a better detection of changes in N-glycosylation profiles of therapeutic glycoproteins.


PMID 27287011

Abstract

Therapeutic proteins are among the top selling drugs in the pharmaceutical industry. More than 60 % of the approved therapeutic proteins are glycosylated. Nowadays, it is well accepted that changes in glycosylation may affect the safety and the efficacy of the therapeutic proteins. For this reason, it is important to characterize both the protein and the glycan structures. In this study, analytical and data processing methods were developed ensuring an easier characterization of glycoprofiles. N-glycans were (i) enzymatically released using peptide-N-glycosidase F (PNGase F), (ii) reduced, and (iii) analyzed by hydrophilic interaction liquid chromatography coupled to a high-resolution mass spectrometer (HILIC-HRMS). Glycosylation changes were analyzed in human plasma immunoglobulin G samples which had previously been artificially modified by adding other glycoproteins (such as ribonuclease B and fetuin) or by digesting with enzyme (neuraminidase). Principal component analysis (PCA) and classification through soft independent modelling by class analogy (SIMCA) were used to detect minor glycosylation changes. Using HILIC-MS-PCA/SIMCA approach, it was possible to detect small changes in N-glycosylation, which had not been detected directly from the extracted-ion chromatograms, which is current technique to detect N-glycosylation changes in batch-to-batch analysis. The HILIC-MS-PCA/SIMCA approach is highly sensitive approach due to the sensitivity of MS and appropriate data processing approaches. It could help in assessing the changes in glycosylation, controlling batch-to-batch consistency, and establishing acceptance limits according to the glycosylation changes, ensuring safety and efficacy. Graphical abstract N-glycosylation characterization using LC-MS-PCA approach.