Suspect and non‐target screening workflows to investigate the in vitro and in vivo metabolism of the synthetic cannabinoid 5Cl‐THJ‐018

Abstract

The use of synthetic cannabinoids causes similar effects as Δ 9 -tetrahydrocannabinol and long-term (ab)use can lead to health hazards and fatal intoxications. As most investigated synthetic cannabinoids undergo extensive biotransformation, almost no parent compound can be detected in urine, which hampers forensic investigations.Limited information about the biotransformation products of new synthetic cannabinoids makes the detection of these drugs in various biological matrices challenging.This study aimed to identify the main in vitro biotransformation pathways of 5Cl-THJ-018 and to compare these findings with an authentic urine sample of a 5Cl-THJ-018 user. The synthetic cannabinoid was incubated with pooled human liver microsomes and cytosol to simulate phase I and phase II biotransformations. Resulting extracts were analyzed with liquid chromatography coupled to quadrupole time-offlight mass spectrometry (LC-QTOF-MS). Three different data analysis workflows were applied to identify biotransformation products. A suspect screening workflow used an in-house database built from literature data and in silico biotransformation predictions. Two non-target screening workflows used a commercially available software and an open-source software for mass spectrometry data processing. A total of 23 in vitro biotransformation products were identified, with hydroxylation, oxidative dechlorination, and dihydrodiol formation pathways as the main phase I reactions.Additionally, five glucuronidated and three sulfated phase II conjugates were identified. The predominant in vivo pathway was through oxidative dechlorination and in total six metabolites of 5Cl-THJ-018 were identified. Biotransformation products both in vitro and in vivo were successfully identified using complementary suspect and non-target screening workflows. KEYWORDS cytosolic fractions, human liver microsomes, In vitro biotransformation, liquid chromatographymass spectrometry, new psychoactive substances Philippe Vervliet and Olivier Mortelé joint first authors.

Compound Characterization and Metabolic Profile Elucidation after In Vitro Gastrointestinal and Hepatic Biotransformation of an Herniaria hirsuta Extract Using Unbiased Dynamic Metabolomic Data Analysis

“…A suspect screening method, based on in silico metabolite prediction, was combined with a nontarget screening workflow to enhance the identification of products formed by in vitro liver biotransformation [41].…”

“…A volcano plot was constructed to plot the p-value from a student t-test as a function of the calculated fold change for every feature. Features with a p-value lower than 0.05 and a log 10-fold change higher than 10 were selected for in depth investigation [41].…”

Perspectives and challenges associated with the determination of new psychoactive substances in urine and wastewater – A tutorial

“…Further exacerbating the determination of these substances is the extent of their metabolism. There have been studies carried out on the metabolism of NPS using human liver microsome incubations to better understand the metabolism of certain NPS [80][81][82][83][84]. In addition, recent advances in computing power have permitted the development of comprehensive knowledge based software to predict the metabolic fate [85,86].…”

“…This is particularly relevant for synthetic cannabinoids and compounds like NBOMes that are highly metabolized in the human body [42,131,132] and for synthetic opioids that are consumed at very low doses [39], leading in both cases to very low concentration levels of the corresponding biomarkers in urine and, consequently, in wastewater. However, there are some published works on the metabolism of NPS [80][81][82][83][84] and different computational tools exist that predicts the metabolic fate of chemicals [86,87]. gives a snapshot and has several limitations that need to be overcome or optimized as previously described.…”

An insight into the metabolism of New Psychoactive Substances : Structural elucidation of urinary metabolites of synthetic cannabinoids and fentanyl analogues using synthesized reference standards

“…[172][173][174][175][176][177][178] Several metabolism studies of synthetic cannabinoids in urine have been carried out, including of ADB-FUBINACA, AM-694, AM-2201, JWH-007, JWH-019, JWH-203, JWH-307, MAM-2201, UR-144, XLR-11, APINAC, BB-22, EG-018, EG-2201, MDMB-CHMCZCA, MDMB-FUBINACA and 5Cl-THJ-018. 134,[156][157][158][159][160][161][162] Many metabolism studies of synthetic cannabinoids have been carried out using combinations of hepatocytes, human liver microsomes, urine and/or blood samples. Included in this list are, 5F-MDMB-PICA, CUMYL-4CN-BINACA, MDMB-4en-PINACA, AB-FUBINACA, AKB-48, 5F-AKB-48, 4′N-5F-ADB, AMB-CHMICA, APINAC, CUMYL-PICA, CUMYL-PINACA, 5F-CUMYL-PINACA, 5F-CUMYL-P7AICA, CUMYL-PEGACLONE, 5F-CUMYL-PEGACLONE, 5F-CUMYL-PICA, CUMYL-4CN-BINACA, MAM-2201, MDMB-CHMICA, MN-18, NM-2201, NNEI, 5F-PY-PICA, STS-135, XLR-11, AM-2201 and UR-144.…”