In 2016, several synthetic cathinones were seized by the State Bureau of Criminal Investigation Bavaria in Germany. Due to their previous appearances in other countries their metabolism was already investigated in human urine as well as differentin vitromodels. These investigations were conducted using ordinary metabolism studies for drugs of abuse by using general knowledge about drug metabolism and visual comparison of mass spectra. The present study aimed to use untargeted metabolomics to support and improve those methods that highly depend on the investigators experience. Incubations were conducted using pooled human liver microsomes (pHLM) and the two cathinones 1-phenyl-2-(1-pyrrolidinyl)-1-butanone and 1-phenyl-2-(1-pyrrolidinyl)-1-heptanone. Samples were analyzed by LC-HRMS/MS using a metabolomics workflow consisting of a reversed phase or normal phase separation followed by electrospray ionization and full scan in positive or negative mode. LC-MS data was afterwards statistically evaluated using principal component analysis, t-distributed stochastic neighborhood embedding, and hierarchical clustering. Significant features were then identified using MS/MS. The workflow revealed 24 significant features after 1-phenyl-2-(1-pyrrolidinyl)-1-butanone and 39 after 1-phenyl-2-(1-pyrrolidinyl)-1-heptanone incubation, consisting of adducts, artifacts, isomers, and metabolites. The applied untargeted metabolomics strategy was able to find almost all of the metabolites that were previously described for 1-phenyl-2-(1-pyrrolidinyl)-1-butanone in literature as well as three additional metabolites. Concerning 1-phenyl-2-(1-pyrrolidinyl)-1-heptanone biotransformation in pHLM, merely four metabolites described in primary human hepatocytes and human urine were not found. This study revealed that untargeted metabolomics workflows are well suited to support biotransformation studies at least of the investigated compounds in pHLM.

The evaluation of liquid chromatography high-resolution mass spectrometry (LC-HRMS) raw data is a crucial step in untargeted metabolomics studies to minimize false positive findings. A variety of commercial or open source software solutions are available for such data processing. This study aims to compare three different data processing workflows (Compound Discoverer 3.1, XCMS Online combined with MetaboAnalyst 4.0, and a manually programmed tool using R) to investigate LC-HRMS data of an untargeted metabolomics study. Simple but highly standardized datasets for evaluation were prepared by incubating pHLM (pooled human liver microsomes) with the synthetic cannabinoid A-CHMINACA. LC-HRMS analysis was performed using normal- and reversed-phase chromatography followed by full scan MS in positive and negative mode. MS/MS spectra of significant features were subsequently recorded in a separate run. The outcome of each workflow was evaluated by its number of significant features, peak shape quality, and the results of the multivariate statistics. Compound Discoverer as an all-in-one solution is characterized by its ease of use and seems, therefore, suitable for simple and small metabolomic studies. The two open source solutions allowed extensive customization but particularly, in the case of R, made advanced programming skills necessary. Nevertheless, both provided high flexibility and may be suitable for more complex studies and questions.

An increasing number of benzodiazepine-type compounds are appearing on the new psychoactive substances market. 8-Chloro-6-(2-fluorophenyl)-1-methyl-4H-[1,2,4]triazolo[4,3-a][1,4]benzodiazepine (well known as flualprazolam) represents a potent ‘designer benzodiazepine’ that has been associated with sedation, loss of consciousness, memory loss and disinhibition. The aims of the present study were to tentatively identify flualprazolam metabolites using in vitro incubations with pooled human liver S9 fraction or HepaRG cells by means of liquid-chromatography-high resolution tandem mass spectrometry. Isozymes involved in phase I and II biotransformation were identified in vitro. Results were then confirmed using human biosamples of an 18-year old male who was admitted to the emergency department after suspected flualprazolam ingestion. Furthermore, the plasma concentration was determined using the standard addition method. Seven flualprazolam metabolites were tentatively identified. Several cytochrome P450 and UDP-glucuronosyltransferase isozymes, amongst them CYP3A4 and UGT1A4, were shown to be involved in flualprazolam biotransformation reactions, and an influence of polymorphisms as well as drug–drug or drug–food interactions cannot be excluded. Alpha-hydroxy flualprazolam glucuronide, 4-hydroxy flualprazolam glucuronide and the parent glucuronide were identified as most abundant signals in urine, far more abundant than the parent compound flualprazolam. These metabolites are thus recommended as urine-screening targets. If conjugate cleavage was performed during sample preparation, the corresponding phase I metabolites should be added as targets. Both hydroxy metabolites can also be recommended for blood screening. The flualprazolam plasma concentration determined in the intoxication case was as low as 8 μg/L underlining the need of analytical methods with sufficient sensitivity for blood-screening purposes.