BACKGROUND: Identifying synthetic cannabinoid designer drug abuse challenges toxicologists and drug testing programs. The best analytical approach for reliably documenting intake of emerging synthetic cannabinoids is unknown. Primarily metabolites are found in urine, but optimal metabolite targets remain unknown, and definitive identification is complicated by converging metabolic pathways. METHODS: We screened 20,017 US military urine specimens collected from service members worldwide for synthetic cannabinoids between July 2011 and June 2012. We confirmed 1432 presumptive positive and 1069 presumptive negative specimens by qualitative liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis including 29 biomarkers for JWH-018, JWH-073, JWH-081, JWH-122, JWH-200, JWH-210, JWH-250, RCS-4, AM2201 and MAM2201. Specimen preparation included enzyme hydrolysis and acetonitrile precipitation prior to LC-MS/MS analysis. We evaluated individual synthetic cannabinoid metabolite detection rates, prevalence, temporal patterns and suitable targets for analytical procedures. RESULTS: Prevalence was 1.4% with 290 confirmed positive specimens, 92% JWH-018, 54% AM2201 and 39% JWH-122 metabolites. JWH-073, JWH-210 and JWH-250 also were identified in 37%, 4% and 8% of specimens, respectively. The United States Army Criminal Investigation Command seizure pattern for synthetic cannabinoid compounds matched our urine specimen results over the time frame of the study. Apart from one exception (AM2201), no parent compounds were observed. CONCLUSIONS: Hydroxyalkyl metabolites accounted for most confirmed positive tests, and in many cases, two metabolites were identified, increasing confidence in the results, and improving detection rates. These data also emphasize the need for new designer drug metabolism studies to provide relevant targets for synthetic cannabinoid identification.
BACKGROUND: Identifying synthetic cannabinoid designer drug abuse challenges toxicologists and drug testing programs. The best analytical approach for reliably documenting intake of emerging synthetic cannabinoids is unknown. Primarily metabolites are found in urine, but optimal metabolite targets remain unknown, and definitive identification is complicated by converging metabolic pathways. METHODS: We screened 20,017 US military urine specimens collected from service members worldwide for synthetic cannabinoids between July 2011 and June 2012. We confirmed 1432 presumptive positive and 1069 presumptive negative specimens by qualitative liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis including 29 biomarkers for JWH-018, JWH-073, JWH-081, JWH-122, JWH-200, JWH-210, JWH-250, RCS-4, AM2201 and MAM2201. Specimen preparation included enzyme hydrolysis and acetonitrile precipitation prior to LC-MS/MS analysis. We evaluated individual synthetic cannabinoid metabolite detection rates, prevalence, temporal patterns and suitable targets for analytical procedures. RESULTS: Prevalence was 1.4% with 290 confirmed positive specimens, 92% JWH-018, 54% AM2201 and 39% JWH-122 metabolites. JWH-073, JWH-210 and JWH-250 also were identified in 37%, 4% and 8% of specimens, respectively. The United States Army Criminal Investigation Command seizure pattern for synthetic cannabinoid compounds matched our urine specimen results over the time frame of the study. Apart from one exception (AM2201), no parent compounds were observed. CONCLUSIONS: Hydroxyalkyl metabolites accounted for most confirmed positive tests, and in many cases, two metabolites were identified, increasing confidence in the results, and improving detection rates. These data also emphasize the need for new designer drug metabolism studies to provide relevant targets for synthetic cannabinoid identification.
Authors: Julie A Marusich; Jenny L Wiley; Timothy W Lefever; Purvi R Patel; Brian F Thomas Journal: Neuropharmacology Date: 2017-11-04 Impact factor: 5.250
Authors: Nadine Schaefer; Mattias Kettner; Matthias W Laschke; Julia Schlote; Andreas H Ewald; Michael D Menger; Hans H Maurer; Peter H Schmidt Journal: Curr Neuropharmacol Date: 2017 Impact factor: 7.363