Etizolam

Anabolic‐androgenic steroids (AASs) are a subclassification of image performance enhancing drugs (IPEDs). While AAS use is most prevalent among people in athletics, there is also high lifetime prevalence of AAS use among prisoners. This study reports the qualitative detection of AASs in seized samples from the Scottish prisons from 2019–2023. Additionally, methods were developed for the quantitative analysis of AASs using gas chromatography–mass spectrometry (GC–MS) and applied to 61 samples of tablets or powders seized from Scottish prisons between July 2022 and July 2023. Since 2022, there has been an increase in AAS detections in the Scottish prisons. Oxymetholone was the most prevalent AAS, followed by metandienone (methandrostenolone, methandienone), methyltestosterone, oxandrolone, mestanolone (methylandrostanolone), stanozolol, and androstenedione. Multiple AASs were found in 21 samples and 10 samples contained other drugs, including amitriptyline, sertraline, zopiclone, mirtazapine, sildenafil, etizolam, Δ9‐tetrahydrocannabinol, and the synthetic cannabinoid MDMB‐INACA. Most AAS samples were tablets (77.0%), although they were also detected in powders, herbal material, e‐cigarettes, and a fragmented soap bar‐type sample. There was a large variation in the concentration of AASs in the tablets and powders seized from the Scottish prisons, demonstrating AASs are another highly variable component of the polydrug use situation in prisons, the effects of which need to be examined further.

Novel psychoactive substances (NPS) pose one of the greatest challenges across the illicit drug landscape. They can be highly potent, and coupled with rapid changes in structure, tracking and identifying these drugs is difficult and presents users with a "Russian roulette" if used. Benzodiazepines, synthetic opioids, synthetic cannabinoids, and synthetic cathinones account for the majority of NPS-related deaths and harm. Detecting these drugs with existing field-portable technologies is challenging and has hampered the development of community harm reduction services and interventions. Herein, we demonstrate that hybridizing fluorescence and reflectance spectroscopies can accurately identify NPS and provide concentration information with a focus on benzodiazepines and nitazenes. The discrimination is achieved through a deep learning algorithm trained on a library of preprocessed spectral data. We demonstrate the potential for these measurements to be made using a low-cost, portable device that requires minimal user training. Using this device, we demonstrate the discrimination of 11 benzodiazepines from "street" tablets that include bulking agents and other excipients. We show the detection of complex mixtures of multiple drugs, with the key example of nitazene + benzodiazepine (metonitazene + bromazolam), fentanyl + xylazine, and heroin + nitazene (etonitazene) combinations. These samples represent current drug trends and are associated with drug-related deaths. When combined with the implementation of detection technology in a portable device, these data point to the immediate potential to support harm reduction work in community-based settings. Finally, we demonstrate that the approach may be generalized to other drug classes outside NPS discrimination.