Glycan Peak Assignment Tool (GPAT)

Significance of the Topic

Glycan profiling by HILIC-FLD is a cornerstone method for characterizing N-glycosylation on therapeutic proteins, a key quality attribute that influences efficacy, stability and immunogenicity. Conventional peak identification based on retention time alone can be ambiguous due to structural isomers and retention shifts. A robust assignment tool that leverages glucose unit (GU) normalization enhances confidence in peak annotation and streamlines glycan analysis workflows.

Objectives and Overview

This application note introduces the Glycan Peak Assignment Tool (GPAT), a free web-based software that predicts GU-normalized retention times for InstantPC- and 2-AB-labeled N-glycans on the Agilent AdvanceBio Amide HILIC column. The primary goals are to reduce reliance on confidential sample details, accelerate peak identification, and improve accuracy through an optional refinement feature that uses two known glycan standards.

Methodology and Instrumentation

Released N-glycans were labeled with either InstantPC or 2-AB reagents and separated by HILIC-FLD. A dextran or maltodextrin ladder (GU4–GU20) provided system calibration. Four chromatographic gradients covered neutral to tetra-sialylated structures. Key instrument settings included:

• Column: Agilent AdvanceBio Amide HILIC, 2.1×150 mm, 1.8 μm at 60°C
• Mobile phases: 50 mM ammonium formate pH 4.4 (A) and acetonitrile (B)
• Flow rate: 0.6 mL/min; injection volumes: 0.5 μl (InstantPC ladder) or 1 μl (samples)
• Detection: InstantPC Ex 285/Em 345 nm; 2-AB Ex 260/Em 430 nm
• System: Agilent 1290 Infinity II LC with high-speed pump, multisampler, multicolumn thermostat and 1260 fluorescence detector

Main Results and Discussion

GPAT contains libraries of GU values for over 100 glycan structures. Without refinement, predicted RT errors ranged up to ~2.5% for late-eluting, highly sialylated glycans. The refinement option—entering RTs for one neutral and one di-sialylated glycan—reduced errors to below 2% across all tested InstantPC and 2-AB samples. In standard mixes, average prediction error after refinement was ~0.6%. When applied to cetuximab N-glycans, 13 InstantPC and 9 2-AB structures were annotated with sub-2% deviation from observed RTs. Narrow GU tolerance windows around unknown peaks highlighted single or few candidate assignments, and further orthogonal data (e.g. MS) can resolve remaining ambiguities.

Benefits and Practical Applications

• Avoids sharing confidential sample details by using ladder-based calibration
• Integrates seamlessly into existing HILIC-FLD workflows
• Reduces time and cost compared to orthogonal identification methods
• Optional refinement maximizes prediction accuracy on a given LC system
• Facilitates automated peak annotation in routine QA/QC and biopharma research

Future Trends and Possibilities

Emerging directions include expanding GU libraries to cover additional labels and uncommon structures, integrating GPAT with mass spectrometry and data analytics platforms, and the application of machine learning to predict retention behavior under varying chromatographic conditions. Development of cloud-based workflows and real-time GU calibration will further accelerate glycan profiling in both academic and industrial laboratories.

Conclusion

The GPAT provides a practical, accurate and user-friendly solution for GU-based assignment of InstantPC and 2-AB N-glycan peaks on the AdvanceBio Amide HILIC column. By combining retention time calibration with an optional refinement step, the tool delivers sub-2% RT prediction error across diverse glycan structures, streamlining glycan analytics in biopharmaceutical quality control and research applications.

References

1. Zhou Q; Qui H. The Mechanistic Impact of N-Glycosylation on Stability, Pharmacokinetics, and Immunogenicity of Therapeutic Proteins. Journal of Pharmaceutical Sciences. 2019;108:1366–1377.
2. Delobel A. Glycosylation of Therapeutic Proteins: A Critical Quality Attribute. In: Mass Spectrometry of Glycoproteins: Methods and Protocols. Springer; 2021. p.1–21.
3. Guile GR; Rudd PM; Wing DR; Prime SB; Dwek RA. A Rapid High-Resolution HPLC Method for Separating Glycan Mixtures and Analyzing Oligosaccharides. Analytical Biochemistry. 1996;240:210–226.
4. Zapun A; Petrescu SM; Rudd PM; Dwek RA; Thomas DY; Bergeron JJM. Conformation-Independent Binding of Monoglucosylated Ribonuclease B to Calnexin. Cell. 1997;88:29–38.
5. Campbell MP; Royle L; Radcliffe CM; Dwek RA; Rudd PM. GlycoBase and autoGU: Tools for HPLC-Based Glycan Analysis. Bioinformatics. 2008;24:1214–1216.
6. Robinson R; et al. Improved Hydrophilic Interaction Liquid Chromatography for LC/FLD/MS Analysis of Released N-Glycans. Agilent Technologies Application Note. 2023;5994-6916EN.