Semi-Automated Glycoproteomic Data Analysis of LC-MS Data Using GlycopeptideGraphMS in Process Development of...

Area of Science:

• Biochemistry
• Analytical Chemistry
• Biotechnology

Background:

• Antibody glycosylation is critical for therapeutic efficacy but complex to analyze.
• Current analytical methods require significant infrastructure and expertise.
• There is a need for simplified, automated analysis in biomanufacturing.

Purpose of the Study:

• To investigate the applicability of a semi-automated data analysis workflow for biologic glycosylation.
• To assess the efficiency of GlycopeptideGraphMS and Progenesis QI for glycopeptide characterization and quantitation.
• To demonstrate the workflow's utility in various bioprocess development scenarios.

Main Methods:

• Utilized a BioAccord LC-MS system for data acquisition.
• Employed GlycopeptideGraphMS and Progenesis QI for semi-automated data analysis.
• Analyzed LC-MS1 datasets of glycopeptides and peptides from various biologic samples.

Main Results:

• Glycopeptide identification and quantitation were achieved in 30 minutes with minimal user intervention.
• Successfully filtered IgG subclasses and identified glycoforms of innovator drugs.
• Demonstrated high correlation between the workflow and conventional released glycan analysis.
• Monitored glycoform of a trastuzumab biosimilar produced in fed-batch cultures.

Conclusions:

• The GlycopeptideGraphMS workflow offers a simplified, semi-automated method for qualitative and quantitative glycopeptide analysis.
• This approach can complement existing multi-attribute monitoring (MAM) tools in bioprocess environments.
• The workflow facilitates efficient biologic glycosylation analysis across development and manufacturing stages.