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Detection of changes in n-glycosylation profiles of therapeutic glycoproteins using LC-MS

par Planinc, Ana
Président du jury Neve, Jean
Promoteur Van Antwerpen, Pierre
Co-Promoteur Delporte, Cédric
Publication Non publié, 2016-12-19

Thèse de doctorat

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Résumé :

Biopharmaceuticals are becoming one of the most promising drugs on the market mainly due to their successful treatment of a vast array of serious diseases, such as cancers, immune disorders, and infections. Structurally, biopharmaceuticals are proteins and it is important to mention that more than 60 % of biopharmaceuticals are glycosylated. Glycosylation is one of the most common posttranslational modifications. It is also the most demanding and the most complex posttranslational modification. The research showed that glycosylation can significantly impact on the safety, efficiency, and quality of the therapeutic glycoproteins. In the first part of the introduction of the present thesis, the development of the therapeutic glycoproteins and their classification were reviewed. Glycosylation process and nomenclature were also discussed. The second part of the introduction revealed current issues in the field of the production and the characterization of the therapeutic glycoproteins. In the context of the doctoral thesis, we introduced new approach, namely hydrophilic interaction liquid chromatography coupled to a high-resolution mass spectrometer (HILIC-HR-MS) combined with Principal Component Analysis (PCA) and classification through Soft Independent Modelling by Class Analogy (SIMCA) data treatment. Accordingly, N-glycans were first enzymatically released using peptide-N-glycosidase F (PNGase F) and reduced using sodium borohydride. Then those N-glycans were separated by HILIC and detected by HR-MS. PCA and SIMCA simplified interpretation of the MS data collected in the huge tables. PCA was applied to test whether it is possible to visualize N-glycosylation differences between samples and to help identifying within which N-glycans changes occurred. SIMCA, which is a more complex data analysis technique, was applied to build and validate a classification models. SIMCA was also applied to verify whether it is possible to use built models to classify real samples. Described approach enabled us to detect small changes in N-glycosylation of the therapeutic glycoproteins (a change of only 1% in relative glycan abundance). It was applied to assess changes in N-glycosylation of therapeutic glycoproteins. Accordingly, we tested N-glycosylation consistency between batches of infliximab, trastuzumab, and bevacizumab and monitored the N-glycosylation of bevacizumab over storage time in plastic syringes.Furthermore, we worked on the faster sample preparation technique, where online-solid-phase extraction (SPE)-LC was combined to the previously mentioned HILIC-MS-PCA/SIMCA method. Online-SPE-LC allowed us to faster the sample preparation in terms of avoiding time-consuming cleaning steps.