Feature-based molecular networking updates the in vitro metabolic characterisation of fenbendazole across species
Feature-based molecular networking (FBMN), an advanced metabolomics tool leveraging MS/MS spectral similarity, was applied to update metabolite characterisation of fenbendazole (FBZ), a veterinary antiparasitic agent with emerging anticancer potential in humans. Despite its therapeutic promise, FBZ's human metabolic pathways remain poorly understood.In this study, FBMN was utilised for the comprehensive <i>in vitro</i> profiling of FBZ metabolites across species, employing high-resolution liquid chromatography-mass spectrometry (LC-HRMS) with data-dependant MS<sup>2</sup> acquisition.Nine metabolites, including two novel sulphate-conjugated forms (M2 sulphate and M7 sulphate), were identified and structurally characterised through integrated FBMN analysis. Oxidative metabolites (M1-M4) were found to be more abundant in rat liver microsomes, whereas monkey hepatocytes exhibited higher levels of most metabolites. Notably, hydrolysed FBZ (M5) dominated human samples, accounting for the largest proportion in both liver microsomes and hepatocytes, suggesting species-specific enzymatic activity.The application of FBMN provided an enhanced, systematic approach for metabolite identification and inter-species comparison, revealing critical metabolic differences that support FBZ biotransformation. These findings offer novel insights into FBZ's metabolic pathways, supporting its safety and efficacy assessment for potential human therapeutic applications.
Authors
- Young‐Heun Jung (ORCID: https://orcid.org/0000-0002-9037-2670)
- Dong-Cheol Lee
- Junyoung O. Park (ORCID: https://orcid.org/0000-0001-9869-8993)
- Ju‐Hyun Kim (ORCID: https://orcid.org/0000-0002-6555-9716)
- Bo-Hyun Choi (ORCID: https://orcid.org/0000-0001-5547-3911)
Institutions
- University of California, Los Angeles (US)
- Daegu Catholic University (KR)
- Yeungnam University (KR)
Publication Details
- Journal
- Xenobiotica
- Published
- 2025-03-16
- DOI
- https://doi.org/10.1080/00498254.2025.2497047
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00