Retrospective Metabolomics Profiling of Clinical Urine Drug Screen Samples Reveals Features Associated with Opiate Exposure

Background/Objectives: Opiates comprise naturally occurring opium alkaloids and their semisynthetic derivatives. Routine urine drug screening relies on enzyme immunoassays (EIAs) to rapidly detect opiate exposure; however, EIAs provide limited insight into opiate-associated metabolic patterns. Methods: We retrospectively analyzed liquid chromatography–quadrupole time-of-flight mass spectrometry (LC-qToF-MS) datasets from comprehensive urine drug screening of 363 patients at the University of Pittsburgh Medical Center Clinical Toxicology Laboratory. Multiple statistical analyses were applied to identify the features associated with opiate (OPIA)-EIA-positive, oxycodone (OXY)-EIA-positive, and 6-monoacetylmorphine (6MAM)-EIA-positive specimens (42, 34, and seven specimens, respectively) designated as EIA-associated discovery feature sets. The feature sets selected by ≥2 statistical analyses were defined as EIA-associated consensus feature set and further evaluated using MS-FINDER for feature annotation. Results: Among 14,883 features, 138, 121, and 104 features were assigned to the OPIA-, OXY-, and 6MAM-EIA discovery feature sets, respectively. Consensus feature sets included oxycodone/opiate metabolites, acetaminophen metabolites, and norfentanyl for OPIA-EIA; oxycodone metabolites, α-phenylalanylaspartic acid, and 4-pyridoxic acid for OXY-EIA; and norfentanyl, 6-monoacetylmorphine, and 3-hydroxycotinine artifact for 6MAM-EIA. Conclusions: These metabolomic patterns indicate a dominant exposure gradient model, in which OXY-EIA-positive specimens primarily reflect prescribed oxycodone exposure, 6-MAM-EIA-positive specimens reflect illicit heroin/fentanyl exposure with polysubstance/recreational use signature, and OPIA-EIA-positive specimens occupy an intermediate, mixed profile shaped by immunoassay cross-reactivity and real-world co-exposures. Associations involving α-phenylalanylaspartic acid and 4-pyridoxic acid are hypothesis-generating and require further validation. These findings illustrate the value of archived clinical toxicology datasets for metabolomic discovery and as a foundation for sentinel laboratory-based surveillance of evolving drug and chemical exposures.