NDLI: 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

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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

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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

Content Provider	Springer Nature Link
Author	Planinc, Ana Dejaegher, Bieke Heyden, Yvan Vander Viaene, Johan Praet, Serge Rappez, Florence Antwerpen, Pierre Delporte, Cédric
Copyright Year	2016
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
Starting Page	477
Ending Page	485
Page Count	9
File Format	PDF
ISSN	16182642
Journal	Analytical and Bioanalytical Chemistry
Volume Number	409
Issue Number	2
e-ISSN	16182650
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-06-10
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Therapeutic glycoproteins N-Glycans Principal component analysis (PCA) Soft independent modelling by class analogy (SIMCA) High-resolution mass spectrometry (HRMS) Hydrophilic interaction liquid chromatography (HILIC) Analytical Chemistry Biochemistry Laboratory Medicine Characterization and Evaluation of Materials Food Science Monitoring/Environmental Analysis
Content Type	Text
Resource Type	Article
Subject	Analytical Chemistry Biochemistry

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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

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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