This pilot study was performed to study the main metabolic reactions of four synthetic cannabinoids: JWH-015, JWH-098, JWH-251, and JWH-307 in order to setup a screening method for the detection of main metabolites in biological fluids. In silico prediction of main metabolic reactions was performed using MetaSite™ software. To evaluate the agreement between software prediction and experimental reactions, we performed in vitro experiments on the same JWHs using rat liver slices. The obtained samples were analyzed by liquid chromatography-quadrupole time-of-flight and the identification of metabolites was executed using Mass-MetaSite™ software that automatically assigned the metabolite structures to the peaks detected based on their accurate masses and fragmentation. A comparison between the experimental findings and the in silico metabolism prediction using MetaSite™ software showed a good accordance between experimental and in silico data. Thus, the use of in silico metabolism prediction might represent a useful tool for the forensic and clinical toxicologist to identify possible main biomarkers for synthetic cannabinoids in biological fluids, especially urine, following their administration.

Metabolism of JWH-015, JWH-098, JWH-251, and JWH-307 in silico and in vitro: a pilot study for the detection of unknown synthetic cannabinoids metabolites

DRAGONI, STEFANIA;PELLEGRINO, Roberto Maria;GORACCI, LAURA;CRUCIANI, Gabriele
2014

Abstract

This pilot study was performed to study the main metabolic reactions of four synthetic cannabinoids: JWH-015, JWH-098, JWH-251, and JWH-307 in order to setup a screening method for the detection of main metabolites in biological fluids. In silico prediction of main metabolic reactions was performed using MetaSite™ software. To evaluate the agreement between software prediction and experimental reactions, we performed in vitro experiments on the same JWHs using rat liver slices. The obtained samples were analyzed by liquid chromatography-quadrupole time-of-flight and the identification of metabolites was executed using Mass-MetaSite™ software that automatically assigned the metabolite structures to the peaks detected based on their accurate masses and fragmentation. A comparison between the experimental findings and the in silico metabolism prediction using MetaSite™ software showed a good accordance between experimental and in silico data. Thus, the use of in silico metabolism prediction might represent a useful tool for the forensic and clinical toxicologist to identify possible main biomarkers for synthetic cannabinoids in biological fluids, especially urine, following their administration.
2014
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1221356
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