Rapid Identification of Major Metabolites of Gefitinib Using Ion Mobility Enabled MS (HDMSE) with the SYNAPT XS

Importance of the Topic

Metabolite profiling and identification are critical in drug discovery and development. Rapid and accurate analysis of drug metabolites ensures that compounds with unfavorable metabolic profiles are eliminated early, reducing cost and time. Ion mobility mass spectrometry adds an orthogonal separation dimension, improving confidence in structural assignments by resolving co-eluting species and providing collision cross-section values.

Objectives and Study Overview

This study demonstrates the application of the SYNAPT XS high-resolution ion mobility-enabled QTof mass spectrometer combined with Waters UNIFI metabolite identification software. Using gefitinib-treated mouse plasma, the goal was to achieve rapid, sensitive, and accurate identification of major drug-related metabolites within a five-minute UPLC-IMS/MS run.

Methodology and Instrumentation

Sample Preparation

• Mouse plasma obtained 2-6 hours after intravenous and oral dosing of gefitinib.
• Protein precipitation with methanol containing formic acid, centrifugation, and dilution.

UPLC Conditions

• Column: 2.1 x 100 mm BEH C18, 1.7 µm, 60 °C.
• Gradient elution from 5 to 50% organic over 2.9 minutes at 650 µL/min.
• Injection volume 2 µL, total run time 5 minutes.

MS and IM Conditions

• Instrument: SYNAPT XS High Resolution Mass Spectrometer.
• Acquisition: HDMSE mode, electrospray positive ionization, m/z 50–1200, resolution mode.
• Ion mobility: nitrogen drift gas, wave height 40 V, wave velocity 650 m/s.
• Data analysis: MassLynx v4.2 and UNIFI 1.9.4 with metabolite identification workflow.

Major Results and Discussion

Ten gefitinib metabolites were detected between 1.72 and 2.51 minutes. The addition of ion mobility separation generated drift-aligned spectra with reduced endogenous interference, exemplified by the o-desmethyl metabolite M523595, where a key product ion at m/z 346.0738 was clearly observed only in drift-aligned data. High mass accuracy (<1 ppm error) and characteristic isotope patterns enabled confident precursor and fragment ion assignment. Major biotransformations included O-demethylation and oxidation of the morpholine ring. Collision cross-section values measured by ion mobility correlated with machine learning predictions (mean absolute error ~1.8%), supporting structural confirmation.

Practical Benefits and Applications

The described workflow offers high throughput with five-minute analysis times, enhanced selectivity through ion mobility separation, and reliable structural elucidation via accurate mass, isotope pattern, and CCS data. This approach accelerates metabolite identification in drug discovery, enabling early safety assessment and reducing resource expenditure.

Future Trends and Opportunities

Advances in CCS prediction algorithms and expanded IMS libraries will further streamline metabolite identification. Integration of artificial intelligence for automated data interpretation promises to reduce manual review. Upcoming developments in hardware sensitivity and resolving power will allow detection of low-abundance metabolites and isomeric species, enhancing confidence in biotransformation studies.

Conclusion

The combination of SYNAPT XS ion mobility-enabled HRMS with UNIFI software provides a rapid, sensitive, and accurate platform for drug metabolite identification. Orthogonal IMS separation improves spectral clarity and the addition of CCS measurements offers an extra confirmatory dimension. This methodology supports efficient drug discovery workflows by delivering high-quality metabolite profiles in abbreviated analysis times.

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