Synthetic cathinones (SCs) constitute a heterogenous class of new psychoactive substances (NPS), structurally related to cathinone. SCs represent the widest NPS class, second to synthetic cannabinoids, accounting for approximately 160 different analogues with substitution at the phenyl group, the amine group, or the alkyl chain. In 2020, α-pyrrolidonophenone analogues were the most trafficked SCs, and were involved in many fatalities and intoxication cases. In particular, 3F-α-pyrrolidinovalerophenone (3F-α-PVP) was the cause of the highest number of SC-related fatal intoxications in Sweden in 2018. Minor structural modifications are used to avoid legal controls and analytical detection, but may also induce different toxicological profile. Therefore, the identification of specific markers of consumption is essential to discriminate SCs in clinical and forensic toxicology. In this study, we assessed 3F-α-PVP metabolic profile. 3F-α-PVP was incubated with 10-donor-pooled human hepatocytes, LC-HRMS/MS analysis, and software-assisted data mining. This well-established workflow was completed by in silico metabolite predictions using three different freeware. Ten metabolites were identified after 3 h incubation, including hydrogenated, hydroxylated, oxidated, and N-dealkylated metabolites. A total of 51 phase I and II metabolites were predicted, among which 7 were detected in the incubations. We suggest 3F-α-PVP N-butanoic acid, 3F-α-PVP pentanol, and 3F-α-PVP 2-ketopyrrolidinyl-pentanol as specific biomarkers of 3F-α-PVP consumption. This is the first time that an N-ethanoic acid is detected in the metabolic pathway of a pyrrolidine SC, demonstrating the importance of a dual targeted/untargeted data mining strategy.

3F-α-pyrrolydinovalerophenone (3F-α-PVP) in vitro human metabolism: Multiple in silico predictions to assist in LC-HRMS/MS analysis and targeted/untargeted data mining / Carlier, J.; Berardinelli, D.; Montanari, E.; Sirignano, A.; Di Trana, A.; Busardo, F. P.. - In: JOURNAL OF CHROMATOGRAPHY. B. - ISSN 1570-0232. - 1193:(2022), p. 123162. [10.1016/j.jchromb.2022.123162]

3F-α-pyrrolydinovalerophenone (3F-α-PVP) in vitro human metabolism: Multiple in silico predictions to assist in LC-HRMS/MS analysis and targeted/untargeted data mining

Carlier J.;Berardinelli D.;Montanari E.;Di Trana A.;Busardo F. P.
2022-01-01

Abstract

Synthetic cathinones (SCs) constitute a heterogenous class of new psychoactive substances (NPS), structurally related to cathinone. SCs represent the widest NPS class, second to synthetic cannabinoids, accounting for approximately 160 different analogues with substitution at the phenyl group, the amine group, or the alkyl chain. In 2020, α-pyrrolidonophenone analogues were the most trafficked SCs, and were involved in many fatalities and intoxication cases. In particular, 3F-α-pyrrolidinovalerophenone (3F-α-PVP) was the cause of the highest number of SC-related fatal intoxications in Sweden in 2018. Minor structural modifications are used to avoid legal controls and analytical detection, but may also induce different toxicological profile. Therefore, the identification of specific markers of consumption is essential to discriminate SCs in clinical and forensic toxicology. In this study, we assessed 3F-α-PVP metabolic profile. 3F-α-PVP was incubated with 10-donor-pooled human hepatocytes, LC-HRMS/MS analysis, and software-assisted data mining. This well-established workflow was completed by in silico metabolite predictions using three different freeware. Ten metabolites were identified after 3 h incubation, including hydrogenated, hydroxylated, oxidated, and N-dealkylated metabolites. A total of 51 phase I and II metabolites were predicted, among which 7 were detected in the incubations. We suggest 3F-α-PVP N-butanoic acid, 3F-α-PVP pentanol, and 3F-α-PVP 2-ketopyrrolidinyl-pentanol as specific biomarkers of 3F-α-PVP consumption. This is the first time that an N-ethanoic acid is detected in the metabolic pathway of a pyrrolidine SC, demonstrating the importance of a dual targeted/untargeted data mining strategy.
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/297588
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