Quantitative N-Glycan Profiling of Therapeutic Monoclonal Antibodies Performed by Middle-Up Level HILIC-HRMS Analysis.
Bastiaan L DuivelshofSteffy DenormeKoen SandraXiaoxiao LiuAlain BeckMatthew A LauberDavy GuillarmeValentina D'AtriPublished in: Pharmaceutics (2021)
The identification and accurate quantitation of the various glycoforms contained in therapeutic monoclonal antibodies (mAbs) is one of the main analytical needs in the biopharmaceutical industry, and glycosylation represents a crucial critical quality attribute (CQA) that needs to be addressed. Currently, the reference method for performing such identification/quantitation consists of the release of the N-glycan moieties from the mAb, their labelling with a specific dye (e.g., 2-AB or RFMS) and their analysis by HILIC-FLD or HILIC-MS. In this contribution, the potential of a new cost- and time-effective analytical approach performed at the protein subunit level (middle-up) was investigated for quantitative purposes and compared with the reference methods. The robustness of the approach was first demonstrated by performing the relative quantification of the glycoforms related to a well characterized mAb, namely adalimumab. Then, the workflow was applied to various glyco-engineered mAb products (i.e., obinutuzumab, benralizumab and atezolizumab). Finally, the glycosylation pattern of infliximab (Remicade®) was assessed and compared to two of its commercially available biosimilars (Remsima® and Inflectra®). The middle-up analysis proved to provide accurate quantitation results and has the added potential to be used as multi-attribute monitoring method.
Keyphrases
- ms ms
- mass spectrometry
- high resolution
- liquid chromatography
- liquid chromatography tandem mass spectrometry
- rheumatoid arthritis
- high performance liquid chromatography
- tandem mass spectrometry
- systemic lupus erythematosus
- high resolution mass spectrometry
- climate change
- risk assessment
- single cell
- quality improvement
- single molecule
- juvenile idiopathic arthritis
- bioinformatics analysis
- highly efficient
- protein protein