ION MOBILITY-ENABLED METABOLITE IDENTIFICATION OF TIENILIC ACID AND TIENILIC ACID ISOMER

Importance of the Topic

Tienilic acid (TA) and its 3-thiophene isomer (TAI) are clinically relevant uricosuric diuretics with distinct hepatotoxic profiles. High-resolution mass spectrometry (HRMS) coupled to ion mobility separation (IMS) offers enhanced selectivity and structural insight for complex metabolite mixtures.

Study Objectives and Overview

This work demonstrates the application of HRMS-IMS for metabolite profiling of TA and TAI in rat urine over 2, 6 and 24 hour timepoints. It integrates ion mobility–derived collision cross section (CCS) measurements with machine learning–based CCS predictions to support structural assignments.

Used Methodology and Instrumentation

• Biological samples: Male Sprague-Dawley rats dosed intravenously at 250 mg/kg; urine collected at 2, 6 and 24 h with vehicle controls.
• Chromatography: Waters ACQUITY UPLC I-Class system, HSS T3 column (1.8 μm, 2.1×100 mm), 0.1% formic acid in water/acetonitrile gradient.
• Mass spectrometry: Vion IMS QTof, ESI positive mode, low-energy collision 6 eV, high-energy ramp 35–55 eV; IMS wave velocity 250 m/s, ramped wave height 20–55 V; data acquired 50–1200 m/z.
• Data processing: UNIFI platform; CCS prediction via SVR and hybrid gradient boosting models in WebMetabase; regression analysis in Spotfire.

Main Results and Discussion

• Diverse biotransformations identified: hydroxylation (+16 Da), O-dealkylation/acetylation (±16 Da), methylation (+30 Da), glycine (+57 Da), glucuronidation (+178–192 Da), glutamine (+144 Da), and a TAI-specific cysteine conjugate.
• Shared and distinct metabolites: common halogenated fragmentation at 188.95 m/z aided distinguishing TA and TAI pathways.
• CCS evaluation: experimentally derived values aligned closely with predictions. Hybrid model yielded R²≈0.96 versus R²≈0.80 for SVR, improving confidence in isomer differentiation.

Benefits and Practical Applications of the Method

IMS-HRMS coupled with predictive CCS modeling streamlines metabolite structural elucidation, enhances isomer resolution and supports comprehensive drug metabolism and toxicology studies.

Future Trends and Potential Applications

• Broader deployment of IMS-DIA workflows for untargeted metabolomics.
• Refinement of machine learning models for CCS prediction across diverse chemical classes.
• Application to biotransformation studies of new investigational drugs and endogenous metabolite profiling.
• Integration with orthogonal separation techniques for enhanced structural elucidation.

Conclusion

The integration of ion mobility and machine learning–based CCS prediction significantly advances metabolite identification, offering robust differentiation of TA and TAI pathways. This approach holds promise for broad adoption in pharmaceutical and toxicological research.

Reference

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