Exact mass, retention time (RT), and collision cross section (CCS) are used as identification parameters in liquid chromatography coupled to ion mobility high resolution accurate mass spectrometry (LC-IM-HRMS). Targeted screening analyses are now more flexible and can be expanded for suspect and non-targeted screening. These allow for tentative identification of new compounds, and in-silico predicted reference values are used for improving confidence and filtering false-positive identifications. In this work, predictions of both RT and CCS values are performed with machine learning using artificial neural networks (ANNs). Prediction was based on molecular descriptors, 827 RTs, and 357 CCS values from pharmaceuticals, drugs of abuse, and their metabolites. ANN models for the prediction of RT or CCS separately were examined, and the potential to predict both from a single model was investigated for the first time. The optimized combined RT-CCS model was a four-layered multi-layer perceptron ANN, and the 95th prediction error percentiles were within 2 min RT error and 5% relative CCS error for the external validation set (n = 36) and the full RT-CCS dataset (n = 357). 88.6% (n = 733) of predicted RTs were within 2 min error for the full dataset. Overall, when using 2 min RT error and 5% relative CCS error, 91.9% (n = 328) of compounds were retained, while 99.4% (n = 355) were retained when using at least one of these thresholds. This combined prediction approach can therefore be useful for rapid suspect/non-targeted screening involving HRMS, and will support current workflows.

High-resolution mass spectrometry (HRMS) is widely used for the drug screening of biological samples in clinical and forensic laboratories. With the continuous addition of new psychoactive substances (NPS), keeping such methods updated is challenging. HRMS allows for combined targeted and non-targeted screening. First, peaks are identified by software algorithms, and identifications are based on reference standard data. Attempts are made to identify the remaining unknown peaks with in silico and literature data. However, several thousand peaks remain where most are unidentifiable or uninteresting in drug screening. The aims of the study were to apply a combined targeted and non-targeted screening approach to authentic driving-under-the-influence-of-drugs (DUID) samples (n = 44) and further validate the approach using whole-blood samples spiked with 11 low-dose synthetic benzodiazepine analogues (SBAs). Analytical data were acquired using ultra-high-performance liquid chromatography coupled with a time-of-flight mass spectrometer (UHPLC-TOF-MS) with data-independent acquisition (DIA). We present a combined targeted and non-targeted screening, where peak deconvolution and filtering reduced the number of peaks to inspect by three orders of magnitude, down to four peaks per DUID sample. The screening allowed for tentative identification of metabolites and drugs not included in the initial screening; 3 drugs and 14 metabolites were tentatively identified in the authentic DUID samples. Running targeted-screening true-positive identifications through the filters retained 73% of identifications. In the non-targeted screening, nine of the spiked SBAs were identified in the concentration range of 0.005-0.1 mg/kg, of which three were tentatively identified at concentrations below those reported in the literature. Copyright © 2016 John Wiley & Sons, Ltd.

The number of new psychoactive substances (NPS) is constantly increasing. However, although the number might be large, most NPS have a low prevalence of use, so keeping screening libraries updated with the relevant analytical targets becomes a challenge. One way to ensure sufficient screening coverage is to use shared high resolution-mass spectrometry (HR-MS) databases, such as HighResNPS.com: a free, online, spreadsheet-format, crowd-sourced HR-MS database for NPS screening. The aims of this study were (i) to present the database to the scientific community and (ii) to verify that the HighResNPS database can be utilized in suspect screening workflows for LC–HR-MS instruments and software from four different instrument vendors. A sample was spiked with 10 NPS, and participating laboratories then analyzed the sample with their respective HR-MS vendor platforms and the HighResNPS database. The HighResNPS data were obtained via a spreadsheet converted to fit the import specifications of the different vendor platforms. Suspect screening was performed using LC–HR-MS vendor platforms from Thermo Fisher, Waters, Bruker and Agilent. All 10 NPS were identified in at least three workflows used for the four different vendor platforms. Multiple users have submitted data to HighResNPS for the same NPS, which resulted in multiple true-positive identifications for these NPS. Suspect screening with LC–HR-MS can be based on diagnostic fragment ions reported by users of different vendor platforms and can support NPS identification in biological samples and/or seizure analyses when no reference standard is available in-house. The present work clearly demonstrates that HighResNPS data is compatible with instruments and screening software from at least four different vendor platforms. The database can thus serve as a useful add-on in LC–HR-MS screening workflows.

Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS)is an important analytical tool in the systematic toxicological analysis performed in forensic toxicology. However, some important compounds, such as the antiepileptic drug valproate (valproic acid; VPA), cannot be directly detected with positive electrospray ionization (ESI + ) due to poor ionization. Here we demonstrate an omics-based retrospective analysis for the identification of indirect screening targets for VPA in whole blood with LC-ESI + -HRMS. Analysis was performed utilizing data acquired across four years from LC-ESI + -HRMS, with VPA results from a quantitative LC-MS/MS method. The combined data with VPA results were split into an exploration set (n = 68; 28% positive) and a test set (n = 37; 32% positive). Eight indirect targets for VPA were identified in the exploration set. The evaluation of these targets was confirmed with retrospective target analysis of the test set. Using a combination of two out of the eight indirect targets, we attained a sensitivity of 92% (n = 12; VPA concentration range: 4.4-29.7 mg/kg) and 100% specificity (n = 25) for VPA with LC-ESI + -HRMS. VPA screening targets were identified with retrospective data analysis and could be appended to the existing screening procedure. A sensitive and specific screening with LC-ESI + -HRMS was achieved with targets corresponding to the sodium adducts of C 7 H 14 O 3 and C 8 H 14 O 3 . Three chromatographic resolved isomer peaks were observed for the latter, and the consistently most intense peak was tentatively identified as 3-hydroxy-4-en-VPA. KEYWORDS biomarker, forensic toxicology screening, indirect screening, retrospective analysis, untargeted high-resolution mass spectrometry screening

The main analytical targets of synthetic cannabinoids are often metabolites. With the high number of new psychoactive substances entering the market, suitable workflows are needed for analytical target identification in biological samples. The aims of this study were to identify the main metabolites of the synthetic cannabinoids, AMB-CHMICA and 5C-AKB48, using an in silico-assisted workflow with analytical data acquired using ultra-high-performance liquid chromatography-(ion mobility spectroscopy)-high resolution-mass spectrometry in data-independent acquisition mode (UHPLC-(IMS)-HR-MS). The metabolites were identified after incubation with rat and pooled human hepatocytes using UHPLC-HR-MS, followed by UHPLC-IMS-HR-MS. Metabolites of AMB-CHMICA and 5C-AKB48 were predicted with Meteor (Lhasa Ltd) and imported to the UNIFI software (Waters). The predicted metabolites were assigned to analytical components supported by the UNIFI in silico fragmentation tool. The main metabolic pathway of AMB-CHMICA was O-demethylation and hydroxylation of the methylhexyl moiety. For 5C-AKB48, the main metabolic pathways were hydroxylation(s) of the adamantyl moiety and oxidative dechlorination with subsequent oxidation to the ω-COOH. The matrix components in the metabolite spectra were reduced with IMS, which improved the accuracy of the spectral interpretation; however, this left fewer fragment ions for assigning sites of metabolism. Meteor was able to predict the majority of the metabolites, with the most notable exception being the oxidative dechlorination and, consequently, all metabolites that underwent that transformation pathway. Oxidative dechlorination of ω-chloroalkanes in humans has not been previously reported in the literature. The postulated metabolites can be used for screening of biological samples, with four-dimensional identification based on retention time, collision cross section, precursor ion, and fragment ions.

Comprehensive drug‐screening performed by liquid chromatography−high resolution mass spectrometry (LC−HRMS) enables identification of hundreds to thousands of drug compounds in a single analysis. Forensic drug screening is generally performed with positive electrospray ionization (ESI+), targeting basic drugs; however, a few toxicologically important drugs such as barbiturates, may require analysis by negative ESI. In this work, screening targets for barbiturates were determined using our LC−HRMS screening with ESI+. For several years, our forensic whole blood samples have been analyzed using the LC−HRMS−ESI+ screening in parallel with a multi‐target LC–MS/MS−ESI− method. From 2014 to 2018, 23 samples were positive for phenobarbital (0.5−81 mg/kg). Retrospective data analysis of 4816 blood samples (15 positive) revealed several potential screening targets for phenobarbital. The targets were tentatively identified by exact mass and isotopic pattern as uncommon adducts of phenobarbital and as a decomposition product of phenobarbital N‐glucoside (C17H24N2O7). Analysis of a test set containing eight positive (0.5–65 mg/kg phenobarbital) and 31 negative samples supported the use of the observed target m/z 323.0614 at 5.14 minutes, corresponding to the [M + HCOONa+Na]+ adduct of phenobarbital. The [M + HCOONa+Na]+ adduct was confirmed as a screening target for common barbiturates, by analysis of barbiturate reference standards in ESI+/ESI−. The [M + HCOONa+Na]+ adduct allowed retrospective analysis with 91% sensitivity (n = 23) and 100% specificity (n = 4855) for phenobarbital in our existing LC−HRMS−ESI+ screening. The two negative results were the two whole‐blood samples with the lowest phenobarbital concentration (<1.8 mg/kg). Thus, a specialized screening is not necessary and use of this adduct likely enables screening for other barbiturates.

Synthetic cannabinoids (SCs) remain the largest class of new psychoactive substances (NPS), and while the number of NPS that are reported to the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) for the first time each year declines, the number of newly reported SCs still exceeds other NPS classes. This decline can be seen as a result of legislative changes by different jurisdictions which have sometimes transitioned to a more generalized approach when controlling substances by defining common structural scaffolds rather than explicit structures. While the consequences of such legislative changes have been expected over the years, the introduction of so‐called “class‐wide” bans puts further pressure on clandestine laboratories to synthesize compounds which are out of the scope of the legislation, and thus, these compounds are initially harder to detect and/or identify in the absence of analytical data. Recently, a SC with an indole‐3‐acetamide core‐linker scaffold, AD‐18 (i.e., ADB‐FUBIATA or ADB‐FUBIACA), was reported for the first time in China in 2021. Here, an additional cannabinoid with the indole‐3‐acetamide scaffold, N‐cyclohexyl‐2‐(1‐pentyl‐1H‐indol‐3‐yl)acetamide (CH‐PIACA), is reported which was identified for the first time in a seized material in Denmark. Structural characterization was performed using gas chromatography–mass spectrometry (GC–MS), liquid chromatography‐high‐resolution mass spectrometry (LC‐HRMS), and nuclear magnetic resonance (NMR) spectroscopy.

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