Despite liquid chromatography–high-resolution tandem mass spectrometry (MS2) enables untargeted acquisition, data processing in toxicological screenings is almost invariably performed in targeted mode. We developed a computational approach based on open source chemometrics software that, starting from a suspected synthetic cannabinoid (SC) determined formula, searches for isomers in different new psychoactive substances web databases, predicts retention time (RT) and high-resolution MS2 spectrum, and compares them with the unknown providing a rank-ordered candidates list. R was applied on 105 SC measured data to develop and validate a multiple linear regression quantitative structure–activity relationship model predicting RT. Competitive Fragmentation Modeling for Metabolite Identification (CFM-ID) freeware was used to predict/compare spectra with Jaccard similarity index. Data-dependent acquisition was performed with an Agilent Infinity 1290 LC-6550 iFunnel Q-TOF MS with ZORBAX Eclipse-Plus C18 (100 × 2.1 mm2/1.8 µm) in water/acetonitrile/ammonium formate gradient. Ability of the combined RT/MS2 prediction to identify unknowns was evaluated on SC standards (with leave-one-out from the RT model) and on unexpected SC encountered in real cases. RT prediction reduced the number of isomers retrieved from a group of new psychoactive substances web databases to one-third (2,792 ± 3,358→845 ± 983) and differentiated between SC isomers when spectra were not selective (4F-MDMB-BUTINACA, 4F-MDMB-BUTINACA 2ʹ-indazole isomer) or unavailable (4CN-Cumyl-B7AICA, 4CN-Cumyl-BUTINACA). When comparing 30/40 eV measured spectra of 99 SC against RT-selected, CFM-ID predicted spectra of isomers, the right candidate ranked 1st on median and 4th on average; 54% and 88% of times the right match ranked 1st or within the first 5 matches, respectively. To our knowledge, this is the first case of extensive chemometrics application to toxicological screening. In most cases, presumptive identification (being based on computation, it requires further information for confirmation) of unexpected SC was achieved without reference measured information. This method is currently the closest possible to true unbiased/untargeted screening. The bottleneck of the method is the processing time required to predict mass spectra (ca. 30–35 s/compound using a 64-bit 2.50-GHz Intel® Core™ i5-7200U CPU). However, strategies can be implemented to reduce prediction processing time.

Although the use, structural variety, and prevalence of synthetic cannabinoids (SCs) have steadily increased on the drug market, they are rarely analyzed in abstinence control programs for driver's license regranting. The aim of this study was to determine the SC prevalence in these programs by analyzing hair samples collected between March 2020 and March 2021 from various regions in Germany, mainly Bavaria (40%). Specimens were analyzed quantitatively for drugs of abuse and qualitatively for 107 SCs. Hair samples were screened by liquid chromatography–tandem mass spectrometry (LC–MS/MS), and to search for unknown SC analogs, positive samples were additionally screened by liquid chromatography‐high resolution time of flight mass spectrometry (LC‐qTOF/MS). The analysis of 5097 hair samples resulted in 181 SC detections (3.6%), showing a wide range of 44 SCs, with up to 13 different compounds found in a single sample. The most prevalent compounds were 5F‐MDMB‐PICA and MDMB‐4en‐PINACA; furthermore, 10 new substances not initially covered by LC–MS/MS analysis were detected by LC‐qTOF/MS. The SC positivity rate was comparable to cocaine (5.4%) and amphetamine (2.6%). Only in 35 cases (0.7%), SC analysis was requested by the clients, highlighting the insufficient coverage of SC consumption in the studied collective. In summary, hair sample analysis proved to be a valuable tool to monitor the use of SCs. In order to keep pace with newly emerging SC analogs, an up‐to‐date analytical method is essential. Prospectively, SCs should be more routinely screened in hair analysis for abstinence control to avoid cannabis substitution by SCs.

The application of Quantitative Structure–Property Relationship (QSPR) modeling to the prediction of reversed-phase liquid chromatography retention behavior of synthetic cannabinoids (SC), and its use in aiding the untargeted identification of unknown SC are described in this paper. 1D, 2D molecular descriptors and fingerprints of 105 SC were calculated with PaDEL-Descriptor, selected with Boruta algorithm in R environment, and used to build-up a multiple linear regression model able to predict retention times, relative to JWH-018 N-pentanoic acid-d₅ as internal standard, under the following conditions: Agilent ZORBAX Eclipse Plus C18 (100 mm × 2.1 mm I.D., 1.8 μm) column with Phenomenex SecurityGuard Ultra cartridge (C18, 10 mm × 2.1 mm I.D., < 2μm) kept at 50°C; gradient elution with 5-mM ammonium formate buffer (pH 4 with formic acid) and acetonitrile with 0.01% formic acid, flow rate 0.5 mL/min. The model was validated by repeated k-fold cross-validation using two-third of the compounds as training set and one-third as test set (Q² 0.8593; root mean squared error, 0.087, ca. 0.56 min; mean absolute error, 0.060) and by predicting relative Retention Times (rRT) of 5 SC left completely out of the modeling study. Application of the model in routine work showed its capacity to discriminate isomers, to identify unexpected SC in combination with mass spectral information, and to reduce the length of the list of candidate isomers to ca. one-third, thus reducing significantly the time required for predicting high-resolution product ion spectra to be compared to the unknown using a computational Mass Spectrometry (MS) search/identification approach.

The emergence of novel synthetic opioids (NSOs) poses significant challenges to public health, toxicologists, and law enforcement. Since the comprehensive scheduling of fentanyl analogues in many countries worldwide, several non-fentanyl NSOs have emerged on the illicit drug market, increasing the risks of addiction and fatal overdoses. Dipyanone, a methadone-like NSO, was first identified in drug seizures in 2021 and later also in postmortem cases. This study aimed to assess the metabolism of dipyanone in humans using in silico predictions, in vitro human hepatocyte incubations, and authentic urine specimens. Dipyanone was primarily metabolised through the opening of the pyrrolidine ring to form the corresponding N-butan-4-ol or N-butanoic acid compound, followed by cyclisation to 4’-[2-ethylidene-5-methyl-3,3-diphenylpyrrolidin-1-yl]butan-1’-ol (EMDPB) and 4’-[2-ethylidene-5-methyl-3,3-diphenylpyrrolidin-1-yl]butanoic acid (EMDPBA); other metabolic transformations included hydroxylation, reduction, and O-glucuronidation. We propose EMDPB and EMDPBA as specific biomarkers of dipyanone consumption. Additionally, dipyanone’s activation of µ- (MOR), κ- (KOR), and δ- (DOR) opioid receptors was evaluated with a homogeneous time-resolved fluorescence-based GTP G_i binding assay. The half-maximal effective concentration (EC₅₀) and maximum effect (E _max) were 96.8 nM and 106% (compared to fentanyl at MOR), 380.4 nM and 13% (compared to U-50488 at KOR), and 1067 nM and 56% (compared to SNC-80 at DOR). Dipyanone’s opioid receptor activation pattern was similar to that of methadone, with a strong agonistic effect at MOR, potentially inducing potent analgesic effects with health risks through the central nervous system and respiratory depression, as well as an abuse and dependence potential. Further pharmacokinetic and pharmacodynamic studies should be conducted to better understand the toxicological relevance of dipyanone.

Benzodiazepines are often used with other drugs like opioids, potentially leading to severe intoxications. Bretazenil, an imidazo-tetrahydropyrrolo-1,4-benzodiazepine, developed in the 1980s but never marketed as a medicine, has recently appeared on the illicit drug market. Given its high potency, short elimination half-life, and potential for rapid metabolism, it is essential to identify markers for bretazenil consumption for clinical and forensic purposes. Our study aimed to thoroughly explore bretazenil’s metabolism using web-based in silico prediction tools, in vitro incubation with pooled human liver microsomes and hepatocytes, and to compare these results with authentic postmortem blood and urine samples. The in silico prediction revealed 16 metabolites, mainly formed by hydroxylation (phase I) and further O -glucuronidation, sulfation, and methylation (phase II) reactions. High-resolution mass spectrometry and software-aided data processing of in vitro and in vivo samples identified a total of 26 metabolites. Eight metabolites were detected in vitro, 15 in postmortem urine, and 11 in postmortem blood. Hydroxylation on the pyrrolidine ring was predominant. Other phase I reactions, including combinations of dihydroxylation, hydroxylation, reduction, and carboxylation as well as phase II glucuronidation and sulfation on the pyrrolidine ring, imidazole ring, or the tert -butyl chain, were also identified. Additionally, we discovered a new benzodiazepine biotransformation pathway via hydroxylation and cysteine conjugation in both human hepatocytes and blood. Due to bretazenil’s extensive metabolism, we recommend hydroxy-bretazenil (B14), reduced hydroxy-bretazenil (B6), and reduced dihydroxy-bretazenil (B1) as significant markers for detecting bretazenil use.

Background: The abuse of psychoactive substances presents challenges in clinical and forensic toxicology. The emergence of novel and potent drugs that pose significant health risks, in particular towards frequent abusers and users unaware of the ingredients, further complicates the situation. Designer benzodiazepines have become a fast-growing subgroup of these new psychoactive substances (NPSs), and their overdose may potentially turn fatal, especially when combined with other central nervous system depressants. In 2021, flubrotizolam, a potent thieno-triazolo designer benzodiazepine, emerged on the illicit market, available online as a “research chemical”. The identification of markers of consumption for this designer benzodiazepine is essential in analytical toxicology, especially in clinical and forensic cases. Methods: We therefore aimed to identify biomarkers of flubrotizolam uptake in ten-donor-pooled human hepatocytes, applying liquid chromatography high-resolution mass spectrometry and software-aided data mining supported by in silico prediction tools. Results: Prediction studies resulted in 10 and 13 first- and second-generation metabolites, respectively, mainly transformed through hydroxylation and sulfation, methylation, and glucuronidation reactions. We identified six metabolites after 3 h human hepatocyte incubation: two hydroxylated metabolites (α- and 6-hydroxy-flubrotizolam), two 6-hydroxy-glucuronides, a reduced-hydroxy-N-glucuronide, and an N-glucuronide. Conclusions: We suggest detecting flubrotizolam and its hydroxylated metabolites as markers of consumption after the glucuronide hydrolysis of biological samples. The results are consistent with the in vivo metabolism of brotizolam, a medically used benzodiazepine and a chloro-phenyl analog of flubrotizolam.