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Updated: Jan 5, 2026

High-throughput and Comprehensive Drug Surveillance Using Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry
Published on: April 23, 2019
Rapid detection and structural characterization of verapamil metabolites in rats by UPLC-MSE and UNIFI platform
Chunyan Zhu1, Mimi Wan2, Huilin Cheng1
1Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, China.
Abstract:
High-resolution mass spectrometry (HRMS) is an important technology for studying biotransformations of drugs in biological systems. In order to process complex HRMS data, bioinformatics, including data-mining techniques for identifying drug metabolites from liquid chromatography/high-resolution mass spectrometry (LC/HRMS) or multistage mass spectrometry (MSn ) datasets as well as elucidating the detected metabolites' structure by spectral interpretation software, are important tools. Data-mining technologies have widely been used in drug metabolite identification, including mass defect filters, product ion filters, neutral-loss filters, control sample comparisons and extracted ion chromatographic analysis. However, the metabolites identified by current different technologies are not the same, indicating the importance of technique integration for efficient and complete identification of metabolic products. In this study, a universal, high-throughput workflow for identifying and verifying metabolites by applying the drug metabolite identification software UNIFI is reported, to study the biotransformation of verapamil in rats. A total of 71 verapamil metabolites were found in rat plasma, urine and faeces, including two metabolites that have not been reported in the literature. Phase I metabolites of verapamil were identified as N-demethylation, O-demethylation, N-dealkylation and oxidation and dehydrogenation metabolites; phase II metabolites were mainly glucuronidation and sulfate conjugates, indicating that UNIFI software could be effective and valuable in identifying drug metabolites.
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