Illicit fentanyl and analogues have been involved in many fatalities and cases of intoxication across the United States over the last decade, and are becoming a health concern in Europe. New potent analogues emerge onto the drug market every year to circumvent analytical detection and legislation, and little pharmacological/toxicological data are available when the substances first appear. However, pharmacokinetic data are crucial to determine specific biomarkers of consumption in clinical and forensic settings, considering the low active doses and the rapid metabolism of fentanyl analogues. Phenylfentanyl is a novel analogue that was first detected in seized material in 2017, and little is currently known about this substance and its metabolism. We studied phenylfentanyl metabolic fate using in silico predictions with GLORYx freeware, human hepatocyte incubations, and liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS). We applied a specific targeted/untargeted workflow using data-mining software to allow the rapid and partially automated screening of LC-HRMS/MS raw data. Approximately 90,000 substances were initially individuated after 3-h incubation with hepatocytes, and 115 substances were automatically selected for a manual check by the operators. Finally, 13 metabolites, mostly produced by N-dealkylation, amide hydrolysis, oxidation, and combinations thereof, were identified. We suggest phenylnorfentanyl as the main biological marker of phenylfentanyl use, and we proposed the inclusion of its fragmentation pattern in mzCloud and HighResNPS online libraries. Other major metabolites include N-Phenyl-1-(2-phenylethyl)-4-piperidinamine (4-ANPP), 1-(2-phenylethyl)-4-piperidinol, and other non-specific metabolites. Phase II transformations were infrequent, and the hydrolysis of the biological samples is not required to increase the detection capability of non-conjugated metabolites. The overall workflow is easily adaptable for the metabolite profiling of other novel psychoactive substances.

In silico prediction, LC-HRMS/MS analysis, and targeted/untargeted data-mining workflow for the profiling of phenylfentanyl in vitro metabolites / Di Trana, A.; Brunetti, P.; Giorgetti, R.; Marinelli, E.; Zaami, S.; Busardo, F. P.; Carlier, J.. - In: TALANTA. - ISSN 0039-9140. - 235(2021), pp. 1-13. [10.1016/j.talanta.2021.122740]

In silico prediction, LC-HRMS/MS analysis, and targeted/untargeted data-mining workflow for the profiling of phenylfentanyl in vitro metabolites

Marinelli E.;Zaami S.;Carlier J.
Ultimo
2021

Abstract

Illicit fentanyl and analogues have been involved in many fatalities and cases of intoxication across the United States over the last decade, and are becoming a health concern in Europe. New potent analogues emerge onto the drug market every year to circumvent analytical detection and legislation, and little pharmacological/toxicological data are available when the substances first appear. However, pharmacokinetic data are crucial to determine specific biomarkers of consumption in clinical and forensic settings, considering the low active doses and the rapid metabolism of fentanyl analogues. Phenylfentanyl is a novel analogue that was first detected in seized material in 2017, and little is currently known about this substance and its metabolism. We studied phenylfentanyl metabolic fate using in silico predictions with GLORYx freeware, human hepatocyte incubations, and liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS). We applied a specific targeted/untargeted workflow using data-mining software to allow the rapid and partially automated screening of LC-HRMS/MS raw data. Approximately 90,000 substances were initially individuated after 3-h incubation with hepatocytes, and 115 substances were automatically selected for a manual check by the operators. Finally, 13 metabolites, mostly produced by N-dealkylation, amide hydrolysis, oxidation, and combinations thereof, were identified. We suggest phenylnorfentanyl as the main biological marker of phenylfentanyl use, and we proposed the inclusion of its fragmentation pattern in mzCloud and HighResNPS online libraries. Other major metabolites include N-Phenyl-1-(2-phenylethyl)-4-piperidinamine (4-ANPP), 1-(2-phenylethyl)-4-piperidinol, and other non-specific metabolites. Phase II transformations were infrequent, and the hydrolysis of the biological samples is not required to increase the detection capability of non-conjugated metabolites. The overall workflow is easily adaptable for the metabolite profiling of other novel psychoactive substances.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1577227
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