Background:3-Methoxyphencyclidine (3-MeO-PCP) and 3-methoxyrolicyclidine (3-MeO-PCPy) are two new psychoactive substances (NPS). The aims of the present study were the elucidation of their metabolic fate in rat and pooled human liver microsomes (pHLM) the identification of the cytochrome P450 (CYP) isoenzymes involved and the detectability using standard urine screening approaches (SUSA) after intake of common users’ doses using gas chromatography-mass spectrometry (GC-MS) liquid chromatography-multi-stage mass spectrometry (LC-MSn) and liquid chromatography-high-resolution tandem mass spectrometry (LC-HR-MS/MS)Methods:For metabolism studies rat urine samples were treated by solid phase extraction or simple precipitation with or without previous enzymatic conjugate cleavage. After analyses via LC-HR-MSn the phase I and II metabolites were identifiedResults:Both drugs showed multiple aliphatic hydroxylations at the cyclohexyl ring and the heterocyclic ring single aromatic hydroxylation carboxylation after ring opening O-demethylation and glucuronidation. The transferability from rat to human was investigated by pHLM incubations where O-demethylation and hydroxylation were observed. The involvement of the individual CYP enzymes in the initial metabolic steps was investigated after single CYP incubations. For 3-MeO-PCP CYP 2B6 was responsible for aliphatic hydroxylations and CYP 2C19 and CYP 2D6 for O-demethylation. For 3-MeO-PCPy aliphatic hydroxylation was again catalyzed by CYP 2B6 and O-demethylation by CYP 2C9 and CYP 2D6 Conclusions:As only polymorphically expressed enzymes were involved pharmacogenomic variations might occur but clinical data are needed to confirm the relevance. The detectability studies showed that the authors’ SUSAs were suitable for monitoring the intake of both drugs using the identified metabolites

The presented study aimed to elucidate the toxicokinetics of the four synthetic cathinones 4chloroethcathinone (4-CEC), N-ethylnorpentylone (N-ethylpentylone, ephylone), N-ethylhexedrone (NEH), and 4-fluoro-alpha-pyrrolidinohexiophenone (4-fluoro-alpha-pyrrolidinohexanophenone, 4-F-PHP, 4F-alpha-PHP, 4F-PHP). Methods First, their metabolism was studied using human urine and blood samples. Analysis of specimens was performed by liquid chromatography-high resolution tandem mass spectrometry (LC-HRMS/MS) and gas chromatography-mass spectrometry (GC-MS). LC-HRMS/MS was also used to analyze in vitro incubations of the new psychoactive substances using pooled human liver S9 fraction (pS9), to identify the monooxygenases involved in the initial metabolic steps, and determination of plasma concentrations after standard addition method. Metabolic stability was tested in pooled human liver microsomes incubations analyzed by LC-ion trap MS. Results Using LC-HRMS/MS, in total 47 metabolites were found in patient samples and pS9 incubations. Using GC-MS, 4-CEC, ephylone, NEH, and five of their metabolites were detectable in urine. The following main phase I reactions were observed: carbonyl group reduction, N-deethylation, hydroxylation, lactam formation (4F-PHP), and demethylenation (ephylone). Mainly glucuronidations were observed as phase II reactions besides conjugates with the dicarboxylic acids malonic, succinic, and glutaric acid (4-CEC), sulfation, methylation (both ephylone), and Nacetylation (NEH). A broad range of monooxygenases was involved in the initial steps with exception of NEH (only CYP1A2 and CYP2C19). 4F-PHP had the shortest in vitro half-life (38 min) and highest intrinsic clearance (15.7 mL×min-1 ×kg-1). Plasma concentrations ranged from 0.8 to 8.5 ng/mL. Conclusions Our results are expected to help toxicologists to reliably identify these substances in case of 3 suspected abuse and allow them a thorough risk assessment.

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 different in vitro models. 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.

Accurate peak picking and further processing is a current challenge in the analysis of untargeted metabolomics using liquid chromatography–mass spectrometry (LC–MS) data. The optimization of these processes is crucial to obtain proper results. This study investigated and optimized the detection of peaks by XCMS, a widely used R package for peak picking and processing of high‐resolution LC–MS metabolomics data by their coefficient of variation using neat standard solutions of drug like compounds. The obtained results were additionally verified by using fortified pooled plasma samples. Settings of the mass spectrometer were optimized by recommendations in literature to enable a reliable detection of the investigated analytes. XCMS parameters were evaluated using a comprehensive parameter sweeping approach. The optimization steps were statistically evaluated and further visualized after principal component analysis (PCA). Concerning the lower concentrated solution in methanol samples, the optimization of both mass spectrometer and XCMS parameters improved the median coefficient of variation from 24% to 7%, retention time fluctuation from 9.3 seconds to 0.54 seconds, and fluctuation of the mass to charge ratio (m/z) from m/z 0.00095 to m/z 0.00028. The number of parent compounds and their related species annotated by CAMERA increased from 88 to 113 while the total amount of features decreased from 3282 to 428. Optimized MS settings such as increased resolution led to a higher specificity of peak picking. PCA supported these findings by showing the best clustering of samples after optimization of both mass spectrometer and XCMS parameters. The results implied that peak picking needs to be individually adapted for the experimental set up. Reducing unwanted variation in the data set was most successful after combining high resolving power with strict peak picking settings.

In vitro and in vivo experiments are widely used for studying the metabolism of new psychoactive substances (NPS). The availability of such data is required for toxicological risk assessments and development of urine screening approaches. This study investigated the in vitro metabolism of the 5 pyrrolidinophenone-derived NPS alpha-pyrrolidinobutyrophenone (alpha-PBP), alpha-pyrrolidinopentiothiophenone (alpha-PVT), alpha-pyrrolidinohexanophenone (alpha-PHP), alpha-pyrrolidinoenanthophenone (alpha-PEP, PV8), and alpha-pyrrolidinooctanophenone (alpha-POP, PV9). First, they were incubated with pooled human liver microsomes (pHLM) or pooled human liver S9 fraction (pS9) for identification of the main phase I and II metabolites. All substances formed hydroxy metabolites and lactams. Longer alkyl chains resulted in keto group and carboxylic acid formation. Comparing these results with published data obtained using pHLM, primary human hepatocytes (PHH), and authentic human urine samples, PHH provided the most extensive metabolism. Second, enzyme kinetic studies showed that the initial metabolic steps were formed by cytochrome P450 isoforms (CYP) CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 resulting in pyrrolidine, thiophene or alkyl hydroxy metabolites depending on the length of the alkyl chain. The kinetic parameters indicated an increasing affinity of the CYP enzymes with increase of the length of the alkyl chain. These parameters were then used to calculate the contribution of a single CYP enzyme to the in vivo hepatic clearance. CYP2C19 and CYP2D6 were mainly involved in the case of alpha-PBP and CYP1A2, CYP2C9 and CYP2C19 in the case of alpha-PVT, alpha-PHP, alpha-PEP, and alpha-POP.

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.

Toxicometabolomics, essentially applying metabolomics to toxicology of endogenous compounds such as drugs of abuse or new psychoactive substances (NPS), can be investigated by using different in vitro models and dedicated metabolomics techniques to enhance the number of relevant findings. The present study aimed to study the toxicometabolomics of the two NPS α-pyrrolidinobutiophenone (1-phenyl-2-(pyrrolidin-1-yl)butan-1-one, α-PBP) and α-pyrrolidinoheptaphenone (1-phenyl-2-(pyrrolidin-1-yl)heptan-1-one, α-PEP, PV8) in HepaRG cell line incubates. Evaluation was performed using reversed-phase and normal-phase liquid chromatography coupled with high-resolution mass spectrometry in positive and negative ionization mode, respectively, to analyze cells and cell media. Statistical evaluation was performed using one-way ANOVA, principal component discriminant function analysis, as well as hierarchical clustering. In general, the analysis of cells did not mainly reveal any features, but the parent compounds of the drugs of abuse. For α-PBP an increase in N-methylnicotinamide was found, which may indicate hepatotoxic potential of the substance. After analysis of cell media, significant features led to the identification of several metabolites of both compounds. Amino acid adducts with glycine and alanine were found, and these have not been described in any study before and are likely to appear in vivo. Additionally, significant changes in the metabolism of cholesterol were revealed after incubation with α-PEP. In summary, the application of metabolomics techniques after HepaRG cells exposure to NPS did not lead to an increased number of identified drug metabolites compared to previously published studies, but gave a wider perspective on the physiological effect of the investigated compounds on human liver cells.

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.