Background: Drug checking is a proven harm reduction strategy and provides real-time information on the market of new psychoactive substances (NPS). It combines chemical analysis of samples with direct engagement with people who use drugs (PWUD), giving the ability to increase preparedness and responsiveness towards NPS. Next to that, it supports rapid identification of potential unwitting consumption. However, NPS cause a toxicological battle for the researchers, as factors such as the unpredictability and quick shift of the market complicate the detection. Methods: To evaluate challenges posed towards drug checking services, proficiency testing was set up to evaluate existing analytical techniques and investigate the capability to correctly identify circulating NPS. Twenty blind substances, covering the most common categories of substances, were analyzed according to the existing protocols of the existing drug checking services, including several analytical methods such as gas chromatography–mass spectrometry (GC-MS) and liquid chromatography with diode array detector (LC-DAD). Results: The proficiency test scores range from 80 to 97.5% accuracy. The most common issues and errors are mainly unidentified compounds, presumably due to no up-to-date libraries, and/ or confusion between structural isomers, such as 3- and 4-chloroethcathinone, or structural analogs, such as MIPLA (N-methyl-N-isopropyl lysergamide) and LSD (D-lysergic acid diethylamide). Conclusions: The participating drug checking services have access to adequate analytical tools to provide feedback to drug users and provide up-to-date information on NPS.

Background: Since late 2019, fortification of ‘regular’ cannabis plant material with synthetic cannabinoid receptor agonists (SCRAs) has become a notable phenomenon on the drug market. As many SCRAs pose a higher health risk than genuine cannabis, recognizing SCRA-adulterated cannabis is important from a harm reduction perspective. However, this is not always an easy task as adulterated cannabis may only be distinguished from genuine cannabis by dedicated, often expensive and time-consuming analytical techniques. In addition, the dynamic nature of the SCRA market renders identification of fortified samples a challenging task. Therefore, we established and applied an in vitro cannabinoid receptor 1 (CB₁) activity-based procedure to screen plant material for the presence of SCRAs. Methods: The assay principle relies on the functional complementation of a split-nanoluciferase following recruitment of β-arrestin 2 to activated CB₁. A straightforward sample preparation, encompassing methanolic extraction and dilution, was optimized for plant matrices, including cannabis, spiked with 5 µg/mg of the SCRA CP55,940. Results: The bioassay successfully detected all samples of a set (n = 24) of analytically confirmed authentic Spice products, additionally providing relevant information on the ‘strength’ of a preparation and whether different samples may have originated from separate batches or possibly the same production batch. Finally, the methodology was applied to assess the occurrence of SCRA adulteration in a large set (n = 252) of herbal materials collected at an international dance festival. This did not reveal any positives, i.e. there were no samples that yielded a relevant CB₁ activation. Conclusion: In summary, we established SCRA screening of herbal materials as a new application for the activity-based CB₁ bioassay. The simplicity of the sample preparation, the rapid results and the universal character of the bioassay render it an effective and future-proof tool for evaluating herbal materials for the presence of SCRAs, which is relevant in the context of harm reduction.

The analysis of heroin samples, before use in the protected environment of user centra, could be a supplementary service in the context of harm reduction. Infrared spectroscopy hyphenated with multivariate calibration could be a valuable asset in this context, and therefore 125 heroin samples were collected directly from users and analysed with classical chromatographic techniques. Further, Mid-Infrared spectra were collected for all samples, to be used in Partial Least Squares (PLS) modelling, in order to obtain qualitative and quantitative models based on real live samples. The approach showed that it was possible to identify and quantify heroin in the samples based on the collected spectral data and PLS modelling. These models were able to identify heroin correctly for 96% of the samples of the external test set with precision, specificity and sensitivity values of 100.0, 75.0 and 95.5%, respectively. For regression, a root mean squared error of prediction (RMSEP) of 0.04 was obtained, pointing at good predictive properties. Furthermore, during mass spectrometric screening, 10 different adulterants and impurities were encountered. Using the spectral data to model the presence of each of these resulted in performant models for seven of them. All models showed promising correct-classification rates (between 92 and 96%) and good values for sensitivity, specificity and precision. For codeine and morphine, the models were not satisfactory, probably due to the low concentration of these impurities as a consequence of acetylation. For methacetin, the approach failed.