Comprehensive investigation on synthetic cannabinoids: Metabolic behavior and potency testing, using 5F-APP-PICA and AMB-FUBINACA as model compounds.
David Fabregat-SafontMarie MardalCarolina NobleAnnelies CannaertChristophe P StoveJuan V SanchoKristian LinnetFélix HernándezMaria IbañezPublished in: Drug testing and analysis (2019)
Synthetic cannabinoids (SCs) represented 45% of new psychoactive substances seizures in Europe (data from 2016). The consumption of SCs is an issue of concern due to their still unknown toxicity and effects on human health, the great variety of compounds synthetized, and the continuous modifications being made to their chemical structure to avoid regulatory issues. These compounds are extensively metabolized in the organism and often cannot be detected as the intact molecule in human urine. The monitoring of SCs in forensic samples must be performed by the analysis of their metabolites. In this work, a workflow for the comprehensive study of SC consumption is proposed and applied to 5F-APP-PICA (also known as PX 1 or SRF-30) and AMB-FUBINACA (also known as FUB-AMB or MMB-FUBINACA), based not only on the elucidation of their metabolites but also including functional data using the NanoLuc approach, previously published. Both cannabinoids were completely metabolized by human hepatocytes (12 and 8 metabolites were elucidated by high resolution mass spectrometry for 5F-APP-PICA and AMB-FUBINACA, respectively) and therefore suitable consumption markers are proposed. The bioassays revealed that 5F-APP-PICA presented lower activity than AMB-FUBINACA at CB1 and CB2 receptors, based on the half maximal effective concentration (EC50 ) and the maximum response (Emax ). These results are in agreement with the different intoxication cases found in the literature for AMB-FUBINACA.
Keyphrases
- human health
- endothelial cells
- ms ms
- high resolution mass spectrometry
- electronic health record
- risk assessment
- induced pluripotent stem cells
- liquid chromatography
- big data
- pluripotent stem cells
- randomized controlled trial
- drinking water
- heart rate
- high resolution
- machine learning
- gas chromatography
- liver injury
- data analysis
- high intensity
- deep learning