Illicit drug purification: Detection and removal of fentanyl analogues from narcotics supply chains

Abstract

Recently, the rate of unintentional overdose deaths in Australia has far surpassed that of population growth, reflecting a public health crisis partially driven by illicit production of fentanyl and analogues, which demand rapid and reliable separation for detection and removal. This review critically investigates current and emerging techniques for the detection of compounds, considers social and legal implications, and examines the potential of Artificial intelligence (AI) to transform fentanyl detection from a reactive to predictive science. Presumptive field methods such as Raman spectroscopy and fentanyl test strips provide near-immediate, qualitative results, aiding emergency response, while laboratory techniques, including coupled mass spectrometry and chromatography, offer superior quantitative precision. Fentanyl’s structural flexibility enables the continual emergence of novel analogues, challenging identification, which relies on preexisting compound libraries, making detection impossible in the field and time-consuming in laboratories. AI offers a promising solution, whereby convolutional neural networks and machine learning can identify and predict unknown compounds. Emerging advancements focus on developing accurate algorithms and improving field-deployable chromatographic systems to ensure forensic-level accuracy. By bridging analytical chemistry, AI, and health policy, separation strategies have the potential to enhance detection and removal of fentanyl analogues, mitigating societal and individual harms of synthetic opioids.