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.

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.

DNA-stabilized silver nanoclusters (DNA-AgNCs) are biocompatible emitters with intriguing properties. However, they have not been extensively used for bioimaging applications due to the lack of structural information and hence predictable conjugation strategies. Here, a copper-free click chemistry method for linking a well-characterized DNA-AgNC to molecules of interest is presented. Three different peptides and a small protein, human insulin, were tested as labeling targets. The conjugation to the target compounds was verified by MS, HPLC, and time-resolved anisotropy measurements. Moreover, the spectroscopic properties of DNA-AgNCs were found to be unaffected by the linking reactions. For DNA-AgNC-conjugated human insulin, fluorescence imaging studies were performed on Chinese hamster ovary (CHO) cells overexpressing human insulin receptor B (hIR-B). The specific staining of the CHO cell membranes demonstrates that DNA-AgNCs are great candidates for bioimaging applications, and the proposed linking strategy is easy to implement when the DNA-AgNC structure is known.

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.