Ion Mobility-Enabled Metabolite Identification of Tienilic Acid and Tienilic Acid Isomer Using Mass-MetaSite and WebMetabase

Importance of the Topic

Accurate metabolite identification is crucial for understanding drug safety and efficacy. Ion mobility spectrometry (IMS) integrated with high-definition MSE (HDMSE) enhances analytical specificity by separating ions based on shape and charge, offering robust data for structural elucidation.

Objectives and Study Overview

This study aimed to profile metabolites of tienilic acid (TA) and its 3-thenoyl isomer (TAI) in rat urine at 2, 6, and 24 hours post intravenous dosing (250 mg/kg). A workflow combining UPLC-IMS-HDMSE acquisition with Mass-MetaSite and WebMetabase software enabled rapid, batch-processed identification of biotransformation products.

Methodology and Instrumentation

The analytical platform comprised a Waters ACQUITY UPLC I-Class PLUS system with an HSS T3 column (1.8 µm, 2.1×100 mm) and a Vion IMS QTof mass spectrometer in ESI+ mode. HDMSE data were acquired using alternating low (6 eV) and elevated (35–55 eV) collision energies. IMS parameters included N₂ drift gas, a wave velocity of 250 m/s, and drift cell voltages ramping 20–55 V. Data processing employed UNIFI v1.9.4, Mass-MetaSite, and WebMetabase v4.0.1 with batch processing for dual-substrate analysis.

Major Results and Discussion

Key metabolites detected for both TA and TAI included mono-hydroxylation (M+16). TA showed additional glucuronides (M+192), ketone reduction glucuronides (M+178), and a rare hydroxy-acetylation product (M+58). TAI produced a unique N-acetylcysteine conjugate (M+163) at the six-hour point. Drift-aligned spectra provided clean precursor and fragment data, improving confidence in structural assignments. Collision cross section (CCS) values distinguished two closely eluting glucuronides of TA, where retention times differed by <1% but CCS by >2.5%.

Benefits and Practical Applications

• Enhanced specificity through drift-time alignment and CCS filtering reduces spectral interference.
• Batch processing accelerates comparative metabolic profiling of multiple compounds.
• Retrospective data mining is enabled by DIA-based HDMSE, preserving full-scan information for future queries.

Future Trends and Opportunities

Expansion of CCS libraries and harmonized databases will support broader metabolite identification workflows. Integration of machine learning algorithms with IMS-HRMS data promises automated annotation of complex biotransformations. Adoption across diverse small molecule applications (e.g., environmental and food analysis) will drive method standardization.

Conclusion

The combination of UPLC-IMS-HDMSE with Mass-MetaSite and WebMetabase offers a powerful, flexible platform for comprehensive metabolite profiling. The integration of IMS-derived CCS metrics enhances separation specificity and structural confidence, advancing drug metabolite identification and safety assessments.

Used Instrumentation

• Waters ACQUITY UPLC I-Class PLUS System
• UPLC HSS T3 Column (1.8 µm, 2.1×100 mm)
• Waters Vion IMS QTof Mass Spectrometer
• UNIFI Scientific Information System v1.9.4
• Mass-MetaSite and WebMetabase v4.0.1

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