違法薬物を即時検出する画期的な携帯型デバイス(Groundbreaking device instantly detects dangerous street drugs, offering hope for harm reduction)

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2025-05-07 バース大学

バース大学の研究チームは、危険な路上薬物を即座に検出できる携帯型デバイスを開発しました。この装置は、合成オピオイドやベンゾジアゼピンなど、既存のモバイル技術では検出が難しい物質を極めて低濃度で特定できます。操作は簡単で、専門知識がなくてもボタン一つで使用可能です。現在、英国、ノルウェー、ニュージーランドの薬物検査サービスで試験運用中です。この技術は、薬物の成分と濃度を即座に分析し、ユーザーが摂取前にリスクを把握できるよう支援します。特に、フェンタニルやニタゼン類など微量でも致死性のある物質の検出が可能で、薬物の混入や偽装による過剰摂取の防止に寄与します。この装置は、クラブや音楽フェスティバル、依存症治療施設などでの活用が期待されており、薬物関連の死亡率低下に貢献することが期待されています。

<関連情報>

ハイブリダイゼーション反射/蛍光分光フィンガープリントのディープラーニングによる不正薬物判別のための現場携帯型技術 Field-Portable Technology for Illicit Drug Discrimination via Deep Learning of Hybridized Reflectance/Fluorescence Spectroscopic Fingerprints

Alexander Power,Matthew Gardner,Rachael Andrews,Gyles Cozier,Ranjeet Kumar,Tom P. Freeman,Ian S. Blagbrough,Peter Sunderland,Jennifer Scott,Anca Frinculescu,Trevor Shine,Gillian Taylor,Caitlyn Norman,Hervé Ménard,Niamh N. Daéid,Oliver B. Sutcliffe,Stephen M. Husbands,Richard W. Bowman,Tom S. F. Haines,and Christopher R. Pudney
Analytical Chemistry  Published: May 7, 2025
DOI:https://doi.org/10.1021/acs.analchem.4c05247

Abstract

違法薬物を即時検出する画期的な携帯型デバイス(Groundbreaking device instantly detects dangerous street drugs, offering hope for harm reduction)

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.

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