Background: Nitazenes are a class of new psychoactive substances (NPS) belonging to the synthetic opioids. It has potent μ- opioid receptor agonist activity.In this study, we investigated an authentic forensic human blood and urine sample from an individual that died from the use of N, N-dimethyl etonitazene.
Objective: To enable rapid analysis in authentic forensic sample, a method was developed utilizing in silico metabolite prediction and liquid chromatography high-resolution mass spectrometry (LC-HRMS) for blood and urine samples.
Method: In this study, LC-HRMS was used to analyse authentic blood and urine samples, and Sygma software was used to predict metabolites. Based on the predicted results, targeted analysis methods of LC-HRMS data were used to study the metabolites of blood and urine.
Result: N, N-dimethyl etonitazene and 7 metabolites were identified in blood and urine samples. Among them, there were four phase I metabolites, which respectively correspond to four metabolic pathways :N-demethylation (M1), 5-amination (M2), 4'-hydroxylation (M4), N-oxidation(M6). There were three phase II metabolites corresponding to two metabolic pathways respectively :acetylation (M3), glucuronidation (M5, M7). M1, M2 and M3 were identified in blood sample, and all metabolites were identified in urine sample.
Conclusion: In this study, Sygma software was used to predict metabolites, and LC-HRMS method was employed to specifically analyse the metabolites of N, N-dimethyl etonitazene in authentic forensic human samples. The time required for data analysis was significantly reduced through in silico metabolite prediction. We recommend the 5-amination metabolite (M2) as a potential biomarker in blood and urine samples of N, N-dimethyl etonitazene. In addition, this study filled the gap in the study of N, N-dimethyl etonitazene metabolism. It also provided real data supplementation for the metabolism of nitazene analogues. The prediction of metabolites by using Sygma provided a certain reference for the future application of artificial intelligence in the field of forensic analysis.
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