Vanderschelden Rachel K, Kundu Reya, Morrow Delaney, Patel Simmi, Tamama Kenichi
Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA.
Clinical Laboratories, University of Pittsburgh Medical Center Presbyterian Hospital, Pittsburgh, PA 15213, USA.
Metabolites. 2025 Aug 22;15(9):563. doi: 10.3390/metabo15090563.
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. 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. 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. 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.
可卡因是一种广泛使用的非法兴奋剂,具有显著毒性。尽管其具有临床相关性,但与可卡因使用相关的更广泛的代谢改变仍未完全明确。本研究旨在通过将非靶向代谢组学应用于回顾性尿液药物筛查数据来识别可卡因暴露的新型生物标志物。我们对匹兹堡大学医学中心临床毒理学实验室363例患者的尿液综合药物筛查(UCDS)原始质谱(MS)数据集进行了回顾性分析。液相色谱 - 四极杆飞行时间质谱(LC-qToF-MS)数据用MS-DIAL进行预处理,并进行多次统计分析以识别与可卡因酶免疫分析(EIA)结果显著相关的特征。使用MS-FINDER对显著特征进行进一步评估以进行特征注释。在14,883个特征中,262个与可卡因-EIA结果显著相关。注释了37个更显著特征的子集,包括已知的可卡因代谢物和杂质、尼古丁代谢物、去甲芬太尼以及一种色氨酸相关代谢物(3-羟基色氨酸)。聚类分析揭示了共同变化的特征,包括母体化合物、代谢物和相关离子种类。识别出了与可卡因暴露相关的特征,包括先前未被充分认识的可卡因代谢物和杂质、共同暴露标志物以及内源性代谢途径的改变。值得注意的是,发现去甲芬太尼与可卡因-EIA显著相关,反映了当前非法药物使用的趋势。本研究强调了将真实世界临床毒理学数据重新用于生物标志物发现的潜力,为识别暴露生物标志物和扩展我们对临床毒理学中药物诱导的代谢紊乱的理解提供了一种有价值的方法。需要使用互补分析平台进行进一步验证和探索。