Metabolite Identification of Complex Cyclic Peptides Using WebMetabase, Ion Mobility-Enabled DIA, and Product Ion Confirmation

Significance of the Topic

The development of cyclic peptide therapeutics relies on robust analytical methods to characterize their metabolic fate. Complex modifications such as non-proteinogenic amino acids, lipid adducts and cyclizations present unique challenges for conventional small-molecule workflows. Integrating ion mobility, data-independent acquisition and cloud-enabled informatics addresses these challenges and accelerates safety and efficacy assessments.

Objectives and Study Overview

This application note evaluates a workflow combining high-definition ion mobility spectrometry (HDMS^E), data-independent acquisition (DIA), targeted product ion confirmation scans and WebMetabase processing. Three FDA-approved cyclic peptides—daptomycin, dalbavancin and lanreotide—serve as test cases to demonstrate rapid metabolite screening, structural assignment and quantitative turnover profiling.

Methodology and Instrumentation

Samples were incubated with simulated intestinal fluid and chymotrypsin, quenched at multiple time points and analyzed by UPLC-ESI-MS using:

• UPLC: ACQUITY UPLC I-Class PLUS with Peptide BEH C18 column
• MS: Vion IMS QTof with alternating low/high collision energies (HDMS^E)
• Drift time calibration and automatic CCS calculation in UNIFI
• QuanRecovery vials to minimize non-specific binding
• WebMetabase and Mass-MetaSite via UNIFI API for automated screening and structural matching

Main Results and Discussion

All three peptides generated detectable catabolites. Daptomycin showed medium turnover (>60% by 300 min), while dalbavancin and lanreotide remained largely intact (<10% turnover). Major metabolic pathways included hydrolysis of ester or amide bonds and demethylation.

• Daptomycin catabolites: prominent +18 Da and –1130 Da species, supported by CCS shifts (≈ 406 Ų)
• Dalbavancin: demethylated forms (–14 Da steps) with CCS ≈ 457 Ų
• Lanreotide: hydrolysis and loss of aromatic moieties, CCS ≈ 356 Ų

HDMS^E resolved overlapping matrix interferences, while targeted product ion confirmation scans (PICS) provided high-quality MS/MS spectra by aligning drift time and m/z information.

Benefits and Practical Applications

This workflow offers:

• Comprehensive detection of multiply charged peptide metabolites without extensive inclusion lists
• Enhanced selectivity through ion mobility separation and CCS tracking
• Automated data processing and web-based review to streamline catabolite identification
• Improved metabolite recovery using low-binding vials

Future Trends and Opportunities

Advances in ion mobility resolution and machine-learning-driven metabolite prediction will further reduce manual review. Integration with cloud platforms can support multi-site studies and cross-laboratory CCS libraries. Expanding this approach to larger biotherapeutics and peptide conjugates will accelerate DMPK profiling in drug discovery.

Conclusion

The combination of ion mobility-enabled DIA, targeted PICS and WebMetabase processing provides an efficient, informative and high-confidence strategy for metabolite identification of complex cyclic peptides. This approach addresses key analytical hurdles and supports rapid ADME profiling essential for modern peptide drug development.

References

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