Retrospective Urine Metabolomics of Clinical Toxicology Samples Reveals Features Associated with Cocaine Exposure

<b>Background/Objectives:</b> Cocaine is a widely used illicit stimulant with significant toxicity. Despite its clinical relevance, the broader metabolic alterations associated with cocaine use remain incompletely characterized. This study aims to identify novel biomarkers for cocaine exposure by applying untargeted metabolomics to retrospective urine drug screening data. <b>Methods:</b> We conducted a retrospective analysis of a raw mass spectrometry (MS) dataset from urine comprehensive drug screening (UCDS) from 363 patients at the University of Pittsburgh Medical Center Clinical Toxicology Laboratory. The liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-qToF-MS) data were preprocessed with MS-DIAL and subjected to multiple statistical analyses to identify features significantly associated with cocaine-enzyme immunoassay (EIA) results. Significant features were further evaluated using MS-FINDER for feature annotation. <b>Results:</b> Among 14,883 features, 262 were significantly associated with cocaine-EIA results. A subset of 37 more significant features, including known cocaine metabolites and impurities, nicotine metabolites, norfentanyl, and a tryptophan-related metabolite (3-hydroxy-tryptophan), was annotated. Cluster analysis revealed co-varying features, including parent compounds, metabolites, and related ion species. <b>Conclusions:</b> Features associated with cocaine exposure, including previously underrecognized cocaine metabolites and impurities, co-exposure markers, and alterations in an endogenous metabolic pathway, were identified. Notably, norfentanyl was found to be significantly associated with cocaine -EIA, reflecting current trends in illicit drug use. This study highlights the potential of repurposing real-world clinical toxicology data for biomarker discovery, providing a valuable approach to identifying exposure biomarkers and expanding our understanding of drug-induced metabolic disturbances in clinical toxicology. Further validation and exploration using complementary analytical platforms are warranted.