An Easy and Fast Approach for Monoclonal Antibody N-linked Glycan Analysis from Sample Preparation to Data Analysis

Importance of the Topic

Therapeutic monoclonal antibodies represent a leading category of biologic drugs with diverse clinical applications
N-linked glycans critically influence antibody stability, bioactivity and immunogenicity
Accurate and rapid glycan profiling is essential for consistent drug quality and regulatory compliance

Study Objectives and Overview

This application note presents a streamlined workflow for N-linked glycan analysis of monoclonal antibodies from sample preparation through data analysis
The goal is to reduce sample preparation time from days to hours while maintaining high resolution and reproducibility

Methodology and Instrumentation

A bevacizumab biosimilar was diluted to 1 mg/mL and processed using the S-Bio EZGlyco mAb-N kit
The fast protocol completes glycan release, 2-AB labeling and cleanup in under three hours without prior IgG purification
Automation is supported via the GlycoAutoPrep system for up to 24 samples per run
Instrumentation:

• Shimadzu Nexera UHPLC system
• HALO Glycan column, 150 mm × 2.1 mm I.D., 2.7 µm
• RF-20A fluorescence detector (Ex 330 nm, Em 420 nm)
• GlycoAutoPrep automated sample preparation

Main Results and Discussion

N-glycan separation resolved nine distinct peaks including oligomannose, hybrid and complex species such as Man3, Man5, G0, G0F, G1Fa and G1FbRepeatability assessment showed relative abundance and retention time RSDs below 2% for all glycan peaks
Relative abundance profiles matched those previously reported, confirming method consistency

Benefits and Practical Applications

The simplified workflow reduces total sample preparation time to under three hours, greatly enhancing laboratory throughput
Improved chromatographic resolution supports accurate quantitation of glycan variants in biopharmaceutical quality control
Automation compatibility facilitates high-throughput glycosylation monitoring in development and manufacturing settings

Future Trends and Opportunities

Further integration of robotic sample preparation and UHPLC analysis will streamline glycan profiling workflows
Advances in labeling chemistries and detection technologies may boost sensitivity for low-abundance glycans
Artificial intelligence and machine learning tools have potential to automate peak identification and data interpretation

Conclusion

This study demonstrates an efficient and robust approach for monoclonal antibody N-linked glycan analysis, achieving complete sample preparation in under three hours and delivering high-resolution, repeatable separations

Reference

Application News AD-0191 Shimadzu 2020 optimized N-glycan separation for bevacizumab biosimilar