LipidQuan™: A Robust LC-MS/MS Methodology for Rapidly Profiling the Lipidome of Liver Tissue Following Metabolism of the Drug Gefitinib

Importance of the Topic

Lipid profiling offers critical insight into physiological and pathological processes, enabling a deeper understanding of disease progression, drug effects and metabolic homeostasis. High-throughput lipidomics supports biomarker discovery and mechanism elucidation in pharmaceutical research and clinical practice.

Objectives and Study Overview

This study applied the LipidQuan™ HILIC-based LC-MS/MS platform to rapidly quantify over 500 biologically relevant lipids in mouse liver tissue following intravenous administration of the EGFR inhibitor gefitinib. The goal was to assess time-dependent alterations in lipid metabolism induced by drug exposure.

Methodology and Instrumentation

Liver samples were collected pre-dose and at 0.5, 1, 3, 8 and 24 hours post-dose. Lipids were extracted via a dichloromethane/methanol protocol with deuterated internal standards. Extracts were analyzed in 8 minutes using HILIC UPLC-MS/MS under both positive and negative ion modes. Key instrumentation:

• ACQUITY UPLC I-Class FTN system with Premier BEH Amide column (2.1 × 100 mm, 1.7 µm)
• Xevo TQ-XS tandem quadrupole mass spectrometer
• TargetLynx™ XS and Skyline for data processing
• MetaboAnalyst and Spotfire for multivariate statistics

Main Results and Discussion

The method quantified 238 lipids in positive mode and 232 in negative mode with linear calibration over three orders of magnitude. Gefitinib and its O-desmethyl metabolite eluted at 0.7 minutes. PCA and PLS-DA revealed clear separation between control and treated groups, indicating significant lipidome dysregulation. Time-series PLS-DA showed a trajectory of metabolic shifts up to 24 hours. Top dysregulated species included free fatty acids (FFAs), phosphatidylethanolamines (PEs), phosphatidylglycerols (PGs), lysophosphatidylethanolamines (LPEs) and lysophosphatidylcholines (LPCs).

Benefits and Practical Applications of the Method

• Robust and reproducible quantification of over 500 lipids
• Rapid 8-minute analysis suitable for high sample throughput
• Broad coverage of lipid classes including low-abundance bioactive lipids
• Reduced method development time via a comprehensive targeted library

Future Trends and Opportunities

Advances may include expansion of targeted libraries, integration of ion mobility separations, coupling with untargeted workflows, and application to diverse tissues and clinical samples. Machine learning and AI could enhance pattern recognition and biomarker discovery from complex lipidomic datasets.

Conclusion

LipidQuan™ provides a fast, reliable workflow for quantitative lipid profiling in tissue samples. Its application to gefitinib-treated mouse liver demonstrated significant, time-dependent lipidome perturbations, underscoring its utility for pharmacometabolomic investigations.

References

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