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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.