ArticleDrug testing and analysis2026
A Novel Workflow for Fast Elucidation of Drug Metabolites for Screening-Combining In Silico Metabolite Prediction With Trapped Ion Mobility QTOF-MS.
Article in Drug testing and analysis, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Urine is one of the preferred matrices for standard toxicological analysis, which makes the inclusion of drug metabolites in targeted and untargeted screening mandatory. Mass spectrometry is key for substance identification, but updating methods for emerging substances like new psychoactive substances (NPS) is challenging due to the limited availability of reference standards for metabolites. This is particularly problematic for drugs that are barely or not detectable at all in urine. Insufficient metabolic knowledge and lack of spectral data carry the risk of false negatives. This study evaluates a non-targeted workflow using ultrahigh-performance liquid chromatography-trapped ion mobility spectrometry time-of-flight mass spectrometry (UHPLC-timsTOF-MS) and dedicated processing software (MetaboScape), integrating in silico metabolite prediction (BioTransformer), fragmentation (MetFrag), collision cross-section (CCS) prediction, and library searching. Quetiapine was selected as a model compound. Phase I metabolites were generated via pooled human liver microsomes (pHLMs) and analyzed by UHPLC-timsTOF-MS. Features were extracted and annotated with MetaboScape. The workflow successfully annotated 20 phase I metabolites in the pHLM assay, with 13 confirmed by library matching and 18 by BioTransformer. These metabolites were added to a targeted UHPLC-QTOF-MS method for analysis of 30 quetiapine-positive ante- and post-mortem urine samples from forensic casework. This revealed N-, O-dealkyl and carboxylated metabolites as the most abundant biomarkers in human urine. This integrated approach enables rapid and reliable metabolite detection, supports biomarker discovery, and facilitates routine screening updates, especially for substances without reference standards. Although not intended for exhaustive metabolic characterization, it offers practical applicability in evolving drug landscapes.
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