B-274 Determining Novel Biomarker Candidates Associated With Heroin and Cocaine Exposure Using Urine Metabolomics

Abstract

Abstract Background Substance abuse poses an escalating health risk in the United States. While acute drug exposure can be tracked using drugs and their metabolites as biomarkers in clinical settings, there is a notable absence of biomarkers for chronic drug exposure or addiction. The development and availability of such biomarkers could significantly enhance clinicians’ ability to identify patients at high risk of addiction, and more accurately predict disease progression and treatment responses. Methods Our study aimed to discover novel biomarkers associated with substance abuse by analyzing raw mass spectrometry (MS) data from urine comprehensive drug screening (UCDS) conducted at the University of Pittsburgh Medical Center (UPMC). The MS data, acquired through untargeted data collection using LC-qToF-MS at the UPMC Clinical Toxicology Laboratory, was initially preprocessed (including peak identification, alignment, and putative annotation) using MS-DIAL to generate a data matrix. This matrix, consisting of objects (cases) and features (m/z and retention time), was further normalized and analyzed using MetaboAnalyst. A combination of statistical methods, including point biserial correlation analysis, volcano plot, significance analysis of microarrays and metabolites (SAM), empirical Bayesian analysis of microarrays and metabolites (EBAM), partial least squares discriminant analysis (PLS-DA), and random forest (RF), were employed to select features significantly associated with the EIA-results. These features were subsequently evaluated for analyte identification using MS-FINDER. Results Our analysis identified nine significant features associated with 6MAM-EIA for heroin abuse, including 6-monoacetylmorphine itself and norfentanyl. For features associated with cocaine metabolite-EIA, 37 significant features were selected, including multiple cocaine metabolites, nicotine metabolites, and norfentanyl. Some of these selected features are likely products of in-source fragmentation or endogenous metabolites. Conclusions Our study has identified a set of potential biomarker candidates for illicit drug exposure. Notably, norfentanyl was found to be significantly associated with both 6MAM-EIA and cocaine metabolite-EIA, reflecting current trends in illicit drug use. Further chemical identification of these biomarkers is planned for future work.