N-linked Glycan Characterization and Profiling: Combining the Power of Accurate Mass, Reference Glucose Units, and UNIFI Software for Confident Glycan Assignments

Significance of the Topic

The structural characterization of N-linked glycans is a critical quality attribute for glycoprotein biotherapeutics. Accurate glycan profiling informs on safety, efficacy, and batch consistency during drug development and quality control. Advances in analytical workflows that combine fluorescence detection with mass spectrometry and bioinformatics platforms enable more reliable and high-throughput glycan analysis.

Objectives and Overview

This study presents a streamlined UPLC-FLR-QTof MS workflow integrated with a retention-time-based Glycan GU Library within the UNIFI Scientific Information System. The method supports automated assignment and profiling of 2-AB labeled N-linked glycans and demonstrates its utility in a comparability study between an innovator etanercept and a biosimilar candidate.

Methodology and Instrumentation

Sample Preparation and Labeling

• Release of N-linked glycans followed by 2-AB labeling using GlycoWorks Reductive Amination kits
• Calibration of retention times via a 2-AB dextran ladder (GU 4–12) and performance standard mixtures

Chromatography and Detection

• UPLC separation on a Glycan BEH Amide column at 40 °C with an acetonitrile/ammonium formate gradient
• In-line fluorescence detection and electrospray ionization positive-mode QTof MS analysis
• Automatic time alignment of FLR and MS data streams based on tubing delay and flow rate

Data Processing and Library Search

• Retention time calibration using fifth-order polynomial or cubic spline fits (R2 > 0.9999)
• Automated GU value calculation and accurate mass confirmation against the Waters Glycan GU Library
• Audit-ready assignments with alternative candidates flagged and manual override capability

Main Results and Discussion

The integrated workflow achieved robust glycan assignments across multiple injections and system configurations. Key findings include

• High reproducibility of GU calibration across runs and instruments
• Automated prioritization of library hits based on GU accuracy and mass confirmation
• Detection of low-abundance glycans via sensitive FLR signals even when MS intensity was low
• Comparability results showing overall similar glycan profiles between innovator and biosimilar etanercept
• Notable increases in high mannose species, including Man6 and Man8, and distinct sialylated structures above 0.1% in the biosimilar batch

These profile differences likely arise from variations in cell culture conditions and may require further functional assays to assess clinical impact.

Benefits and Practical Applications

This solution offers

• Comprehensive, compliant-ready glycan assignments with minimal manual intervention
• Streamlined reporting of relative glycan abundances and structures without external data handling
• Enhanced laboratory efficiency and data integrity for routine QC and biosimilar comparability studies

Future Trends and Possibilities

Emerging capabilities will further enrich glycan characterization

• Integration of data-dependent MS/MS for detailed isomer discrimination
• Exoglycosidase array workflows to confirm linkage and branching patterns
• Expansion of custom GU libraries for novel or host-specific glycan structures
• Application of machine learning algorithms for predictive glycan profiling and quality control

Conclusion

The combined UPLC-FLR/QTof MS workflow with UNIFI and a retention-time-based glycan library addresses key challenges in N-glycan profiling. The approach delivers high-confidence assignments, reproducible calibration, and efficient reporting, supporting both individual sample analyses and large-scale comparability studies in biopharmaceutical research and quality control.

References

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