Time-Series Analysis of Fentanyl Concentration in the Unregulated Opioid Drug Supply in a Canadian Setting.

North America has been contending with an unregulated street drug supply in which opioids are often adulterated with illicitly manufactured fentanyl. The unpredictability of composition may result in an increased risk of overdose due to unexpected elevated concentrations of the high-potency drug. Using data from a community-based drug-checking project, we evaluated trends in fentanyl concentration of illicit opioids in the context of an overdose epidemic. Using a quantification model for fentanyl hydrochloride, historical Fourier-transform infrared spectra from opioid drug-checking samples were analyzed to determine fentanyl concentrations. Median monthly fentanyl concentrations were plotted, and polynomial and autoregressive time-series analyses were performed to examine trends over time. A total of 3,621 fentanyl-positive samples were included in the study, spanning November 2017 to December 2019. Monthly median fentanyl concentrations ranged from 4.5% to 10.4%. Time-series analyses indicated that a third-degree polynomial model fit the data well (R2 = 0.639), suggesting a cyclical pattern in median concentration over time. Notably, absolute variance in fentanyl concentration decreased by an average 0.1% per month (P < 0.001). Future research should explore the relationship between fentanyl concentration and overdose to identify potential targeted harm-reduction interventions that can respond to changes in observed fentanyl concentration.

Development of a neural network model to predict the presence of fentanyl in community drug samples.

INTRODUCTION: Increasingly, Fourier-transform infrared (FTIR) spectroscopy is being used as a harm reduction tool to provide people who use drugs real-time information about the contents of their substances. However, FTIR spectroscopy has been shown to have a high detection limit for fentanyl and interpretation of results by a technician can be subjective. This poses concern, given that some synthetic opioids can produce serious toxicity at sub-detectable levels. The objective of this study was to develop a neural network model to identify fentanyl and related analogues more accurately in drug samples compared to traditional analysis by technicians. METHODS: Data were drawn from samples analyzed point-of-care using combination FTIR spectroscopy and fentanyl immunoassay strips in British Columbia between August 2018 and January 2021. We developed neural network models to predict the presence of fentanyl based on FTIR data. The final model was validated against the results from immunoassay strips. Prediction performance was assessed using F1 score, accuracy, and area under the receiver-operating characteristic curve (AUROC), and was compared to results obtained from analysis by technicians. RESULTS: A total of 12,684 samples were included. The neural network model outperformed results from those analyzed by technicians, with an F1 score of 96.4% and an accuracy of 96.4%, compared to 78.4% and 82.4% with a technician, respectively. The AUROC of the model was 99.0%. Fentanyl positive samples correctly detected by the model but not by the technician were typically those with low fentanyl concentrations (median: 2.3% quantity by weight; quartile 1-3: 0.0%-4.6%). DISCUSSION: Neural network models can accurately predict the presence of fentanyl and related analogues using FTIR data, including samples with low fentanyl concentrations. Integrating this tool within drug checking services utilizing FTIR spectroscopy has the potential to improve decision making to reduce the risk of overdose and other negative health outcomes.

Drug checking identifies counterfeit alprazolam tablets.

BACKGROUND: Non-prescribed benzodiazepine use is increasing in North America, especially among youth. Owing to increasing demand, counterfeit benzodiazepine tablets are mass-produced in clandestine, unregulated environments and sold as legitimate pharmaceuticals. This study aimed to examine the contents of counterfeit alprazolam tablets available in the unregulated drug market in British Columbia, Canada. METHODS: Data were collected from an ongoing evaluation of a community drug checking service in British Columbia between October 2017 and March 2020. The service operates point-of-care in harm reduction sites using Fourier-transform infrared (FTIR) spectrometers coupled with fentanyl and benzodiazepine immunoassay strips. A subset of samples were sent for confirmatory analysis at partner laboratories and underwent one or more of gas chromatography/mass spectrometry, liquid chromatography/mass spectrometry, and quantitative nuclear magnetic resonance analysis. RESULTS: During the study period, 10,814 total samples were submitted for drug checking, 139 of which were expected to be Xanax (alprazolam) or generic tablets and met the criteria for inclusion. Using FTIR analysis, 33 (23.7 %) samples were identified to contain alprazolam. Only 122 samples were checked using benzodiazepine immunoassay strips and 88 (72.1 %) tested positive. Qualitative results from the 20 samples submitted for confirmatory analysis included various new psychoactive substances and only 2 contained only alprazolam. CONCLUSIONS: Our findings provide evidence that Xanax tablets obtained from the unregulated drug market are likely to be counterfeit and may not contain alprazolam. Drug checking offers people who use drugs a valuable means to determine the contents of their substances; however, limitations of point-of-care technologies must be considered.