Rapid Metabolite Tof Screening, Structure Elucidation and Clearance Estimates Using Informatics-HRMS Approaches

Importance of the Topic

The ability to rapidly identify and quantify drug metabolites is essential for drug discovery and development. High-resolution mass spectrometry (HRMS) combined with informatics tools accelerates structure elucidation, supports safety assessment by detecting reactive intermediates, and informs pharmacokinetic modeling across species.

Study Objectives and Overview

• Demonstrate a streamlined workflow for comprehensive metabolite screening in Phase I and Phase II metabolism.
• Apply the workflow to glyburide, buspirone, clozapine, and verapamil incubations in human and preclinical species.
• Showcase cross-species comparisons and clearance estimation capabilities.

Methodology

Test compounds were incubated with human, rat, dog, or monkey liver microsomes. Glutathione (GSH) was added to trap potential reactive metabolites. Protein precipitation with acetonitrile followed by centrifugation generated clear supernatants for analysis. Chromatographic separation employed a water/acetonitrile gradient (5–60% acetonitrile with 0.1% formic acid over 8 minutes) at 0.6 mL/min and 60 °C using a BEH C18 column.

Used Instrumentation

• Waters Acquity I-Class UPLC system
• Acquity BEH C18 column (1.7 µm, 2.1 × 100 mm)
• Waters Xevo G2-S QTof mass spectrometer operating in MSE mode (ESI+, 32,500 resolution, low CE 2 eV, high CE ramp 10–30 eV, 0.1 s scan time)
• UNIFI software for data acquisition, charge-state deconvolution, and automated processing

Main Results and Discussion

The informatics platform automatically detected and grouped metabolite charge states and adducts, streamlining data interpretation:

• Glyburide: Display of all identified metabolites, oxidative (+O) subset, structure heatmap, binary plot, and full chromatogram.
• Buspirone: Summary table of metabolites, chromatograms for total and +O2 subsets, and high/low energy MS spectra for a selected +O metabolite (r.t. 2.38 min).
• Clozapine: Identification of Phase I metabolites and GSH adducts with overlaid trend plots showing adduct formation over time.
• Verapamil: Fragmentation mapping, bar plots of concentration changes across species and time, and export of quantitative data for clearance estimation in Phoenix WinNonlin.

Benefits and Practical Applications

This integrated HRMS-informatics workflow offers:

• Rapid metabolite profiling across multiple species and metabolic phases.
• Efficient detection of reactive intermediates via GSH trapping.
• Automated data processing that reduces manual interpretation and supports data consistency.
• Exportable quantitative results for pharmacokinetic modeling and DMPK decision making.

Future Trends and Opportunities

Ongoing developments may include the integration of machine learning algorithms for improved metabolite prediction, expansion to other biological matrices (e.g., plasma, urine), and real-time data streaming to support continuous manufacturing quality control. Enhanced species translation models and automated clearance prediction can further accelerate early‐stage drug development.

Conclusion

The presented informatics-HRMS platform enables comprehensive, multivariate metabolite screening and quantification. Its automated data processing and visualization capabilities make it a versatile tool for drug metabolism and pharmacokinetic studies across compounds, species, and time points.

Reference

• Yun W. Alelyunas, Mark D. Wrona, Paul Rainville; AAPS Poster # W4297, Waters Corporation, 2023