Development of a Neural Network Model to Predict the Presence of Fentanyl in Community Drug Samples
Lianping Ti, Cameron J. Grant, Samuel Tobias, Dennis K. Hore, Richard Laing, Brandon D. L. Marshall
Abstract
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.
Introduction
Recent decades have seen a rapid shift in the unregulated drug market, with the emergence of novel synthetic opioids (e.g., fentanyl, carfentanil) and their associated harms (e.g., overdose, mortality) [1–3]. In response, drug checking services have been implemented as an important harm reduction intervention for people who use drugs to chemically analyze their substances and receive fact-based information and consultation regarding the compounds detected in their sample [4–6]. Not a new concept, the vast majority of drug checking services have previously focused on drugs used in party and festival settings (e.g., psychedelics, stimulants) [7, 8], so less is known about drug checking in the context of the synthetic opioid-driven overdose epidemic. While limited, recent studies have started to show a positive impact of drug checking, including increased engagement in overdose risk reduction practices following the use of the service in some communities [5, 9–11].
Material and methods
British Columbia (BC), the westernmost province of Canada, has been disproportionately affected by the opioid-driven overdose epidemic [3]. By the time a public health emergency was declared in 2016, a significant number of lives had already been lost to illicit drug toxicity, largely due to the widespread presence of fentanyl and other novel synthetic opioids in the unregulated drug supply [29]. In response, a diverse range of harm reduction interventions, including supervised consumption sites, take-home naloxone programs, and drug treatment have been scaled-up [30–32]. As a result, illicit drug toxicity death rates were decreasing between 2018 and 2019 from 31.2 per 100,000 to 19.4 per 100,000, respectively; however, the emergence of the COVID-19 pandemic and corresponding social distancing and self-isolation measures resulted in a significant rise in death rates from 34.4 per 100,000 in 2020 to 44.1 per 100,000 in 2021 [3, 33].
Results
In total, 12,684 samples were included in our study: 6,099 (48.1%) were identified as fentanyl positive by immunoassay strips. Shown in Table 1, the large majority of samples were checked in the Vancouver Coastal Health Authority region (11,092; 87.4%). Among fentanyl immunoassay strip positive samples, the most common color and texture were purple (1,300; 23.0%) and pebbles (3,010; 40.4%), respectively, whereas for fentanyl immunoassay strip negative samples the most common color and texture were white (3,552; 53.9%) and powder (2,483; 37.7%), respectively.
Discussion
In summary, our findings demonstrate that a neural network model was able to achieve highly promising and accurate results in predicting the presence of fentanyl in drug samples analyzed using FTIR data files, with an F1 score of 96.4%. Interestingly, the model was typically able to detect very low concentrations of fentanyl and analogues, with the majority of samples falling below the detection limit of FTIR spectroscopy [16, 17]. Previous data in this setting have indicated the emergence of highly potent fentanyl analogues (e.g., carfentanil) [3, 38, 39], which may be present in drug samples at very low concentrations which are not captured by trained technicians using FTIR. However, fentanyl immunoassay strips, with their high sensitivity and specificity to potent fentanyl analogues, account for this limitation of FTIR, hence the widespread use of these technologies in tandem [15, 16].
Conclusion
In sum, we developed a neural network model that can accurately predict the presence of fentanyl using absorbance spectrum data files obtained from FTIR spectroscopy. The model was able to correctly detect fentanyl-positive samples below the limit of detection of the FTIR spectrometer. Our findings point to the potential of integrating this tool within drug checking services utilizing FTIR spectroscopy to improve decision making and reduce harms associated with overdose and other negative health outcomes.
Acknowledgments
We offer thanks to those individuals who participated directly in the study by having their drugs analyzed, with the hopes that this involvement will contribute to utilizable public health information, improved harm reduction care, and potentially, decreased loss of life. We would also like to thank researchers and staff at various community organizations, health authorities, and laboratory services across the province for their work in this area. Health Canada Drug Analysis Service provided confirmatory testing services; however, the findings reported here should in no way be taken as an endorsement of the specific point-of-care technologies that were used for this study.
Citation: Ti L, Grant CJ, Tobias S, Hore DK, Laing R, Marshall BDL (2023) Development of a neural network model to predict the presence of fentanyl in community drug samples. PLoS ONE 18(7): e0288656. https://doi.org/10.1371/journal.pone.0288656
Editor: Muhammad Hanif, Bahauddin Zakariya University, PAKISTAN
Received: January 27, 2023; Accepted: June 30, 2023; Published: July 13, 2023
Copyright: © 2023 Ti et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data table files containing wavenumber and corresponding absorbance for each spectrum (i.e., drug sample) and corresponding immunoassay strip results are available from the following public repository on GitHub: https://github.com/DrugCheckingBC/nn-fentanyl-prediction.
Funding: The study was supported by the Health Canada Substance Use and Addictions Program (1718-HQ-000024; https://www.canada.ca/en/health-canada/services/substance-use/canadian-drugs-substances-strategy/funding/substance-use-addictions-program.html), Vancouver Foundation (https://www.vancouverfoundation.ca), and the US National Institutes of Health-National Institute on Drug Abuse (R01DA052381; https://nida.nih.gov). The content is solely the responsibility of the authors and does not necessarily represent the official views of these funding agencies. LT is supported by a Michael Smith Health Research British Columbia (MSHRBC; https://healthresearchbc.ca) Scholar Award. The funding organizations had no role in the design, data collection, analysis, or preparation of the manuscript. The co-author (RL) who is affiliated with Health Canada is not a co-investigator of the grants that funded our study.
Competing interests: The authors declare that no competing interests exist.
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0288656#abstract0
