Author: Almheiri, Salama B ; Al Ali, Auhood Y ; Altamimi, Mohamad J ; Alremeithi, Rashed H ; Ahli, Hessa A ; Alkhuwaildi, Buthaina M ; Almazrouei, Eman S ; Almadani, Fatma M

Over the past decade, the global use of synthetic cannabinoid receptor agonists (SCRA) has surged, driven by factors such as affordability, ease of synthesis, and discreet online distribution. These substances are designed to imitate the effects of cannabis; however, they belong to entirely different classes of synthetic compounds, are much more potent and are linked to severe health consequences, including psychological addiction, acute behavioral issues, fatal and non-fatal overdose, and long-term neurological damage. Their wide availability, strong potency, and ability to avoid detection classify them as high-risk drugs. This research focuses on demonstrating the impacts of proactive scheduling of SCRA on trends in police seizures of these substances in Dubai, United Arab Emirates (UAE), between 2011 and 2022. 687 samples from 317 cases were analyzed by GC-MS, and 64 distinct SCRA were detected. Most SCRA were found to be added to herbal materials or in paper or liquid forms. There was a steady increase in SCRA police seizures over time, mirroring global trends. The most commonly detected SCRA evolved over the study period, with JWH-019 predominating in 2011, followed by AM-2201 (2012-2013), AKB48 (2014), AB-CHMINACA (2015), 5-fluoro AKB48 analogues (2016-2018), 5F-MDMB-PICA (2019), 5-fluoro MDMB-PINACA (2020), and MDMB-4en-PINACA by the end of the study period (2021-2022). The impact of legislation on the presence of SCRA in Dubai's drug market was illustrated using a heat map that showed how SCRA that are added to the drug schedules promptly disappear from the market, but are rapidly replaced by new SCRA. In conclusion, the increasingly rapid development of the SCRA drug market needs to be continually monitored. While legislative action, such as that implemented in the UAE, appears to inhibit the availability of specific SCRA, the data suggest a 'cat and mouse' dynamic whereby the prohibition of individual compounds is followed by the emergence of new, lesser-known SCRA, highlighting the need for broader and more adaptive regulatory strategies.

11 Nov 2025·ANALYTICAL CHEMISTRY

AI-SNPS2: A Multi-Layered LC-MS/MS Platform Integrating Molecular Networking and Retention Time Prediction for Identifying Controlled and New Psychoactive Substances

Article

The emergence of unknown controlled substances poses a significant challenge in forensic and analytical sciences. While liquid chromatography-tandem mass spectrometry (LC-MS/MS) enables identification of compounds through spectral database matching, it remains limited for synthesized analogues not present in existing libraries. To address this gap, we developed AI-SNPS2 (Artificial Intelligence Screener for Narcotic Drugs and New Psychoactive Substances 2), an enhanced version of our previously reported screening software. AI-SNPS2 is structured into five integrated layers: LC-MS Viewer, AI Classifier, Identifier, NetBuilder (a GNPS-inspired molecular networking module), and RT Predictor (a machine learning-based retention time prediction module). These layers allow structural analogue detection via spectral similarity and chromatographic plausibility filtering, thereby extending identification capabilities beyond conventional spectral search. The RT Predictor layer incorporates four regression models─artificial neural network (ANN), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost)─trained on 42 molecular descriptors from 164 controlled substances. All models exhibited strong performance, with XGBoost achieving the highest accuracy (R² = 0.964, MAE = 0.585). When applied with the RT calibration function, deviations were typically within a few minutes on a 110 min gradient, demonstrating the RT predictor's utility for candidate filtering. The software's utility was further evaluated by spiking JWH-019, JWH-015, and JWH-302 into two complex matrices; both were successfully identified through integration of molecular networking (MN) and hybrid similarity search (HSS) algorithm. Furthermore, evaluation using five additional compounds demonstrated that AI-SNPS2 is a highly promising tool for detecting compounds absent from existing databases.

01 May 2025·MICROCHIMICA ACTA

Simultaneous enrichment and ultra-high sensitivity detection of multi-structural synthetic cannabinoids in large-volume wastewater

Article

Author: Mengyi Chen ; Rui Cao ; Siyu Li ; Deli Xiao ; Jiawei Chen ; Bin Di ; Ningjia Pang

Nowadays, the abuse of synthetic cannabinoids has aroused the world's great attention. However, the determination of synthetic cannabinoids in wastewater faces the bottleneck of complex species and very low content. Therefore, this work integrated GO-Fe₃O₄ with an ionic liquid (ILs-GO-Fe₃O₄) to create a mixed hemimicelles magnetic solid-phase extraction (MHMSPE) system for the detection of synthetic cannabinoids (SCs) in wastewater samples using ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Because of the multiple interaction sites of mixed hemimicelles' magnetic solid-phase extraction materials, multi-structural synthetic cannabinoids can be enriched simultaneously. Under the optimized conditions, the limits of quantitation (LOQ) of the method were as low as 10-50 pg/L, lower than other methods. The average recoveries of 18 analytes were in the range 71.23-96.08% with relative standard deviations (RSD) lower than 15% (n = 6). To our knowledge, this is the first development of magnetic solid-phase extraction for large-volume samples pretreatment (1 L) and ultra-high sensitive detection of synthetic cannabinoids (10 pg/L), due to the high enrichment factor (10,000-fold). The extraction process was convenient, accurate, and rapid, and offers significant potential for ultra-high sensitivity detection in large-volume samples, providing a valuable tool for simultaneous broad-spectrum extraction and drug abuse monitoring.

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