Trace Detection of Adulterants in Illicit Opioid Samples Using Surface-Enhanced Raman Scattering and Random Forest Classification.

The detection of trace adulterants in opioid samples is an important aspect of drug checking, a harm reduction measure that is required as a result of the variability and unpredictability of the illicit drug supply. While many analytical methods are suitable for such analysis, community-based approaches require techniques that are amenable to point-of-care applications with minimal sample preparation and automated analysis. We demonstrate that surface-enhanced Raman spectroscopy (SERS), combined with a random forest classifier, is able to detect the presence of two common sedatives, bromazolam (0.32-36% w/w) and xylazine (0.15-15% w/w), found in street opioid samples collected as a part of a community drug checking service. The Raman predictions, benchmarked against mass spectrometry results, exhibited high specificity (88% for bromazolam, 96% for xylazine) and sensitivity (88% for bromazolam, 92% for xylazine) for the compounds of interest. We additionally provide evidence that this exceeds the performance of a more conventional approach using infrared spectral data acquired on the same samples. This demonstrates the feasibility of SERS for point-of-care analysis of challenging multicomponent samples containing trace adulterants.

Beyond a spec: assessing heterogeneity in the unregulated opioid supply.

BACKGROUND: Drug checking services aim to provide compositional information for the illicit drug supply and are being employed in public health responses to extreme rates of overdose associated with fentanyl within street opioids. The technologies used within these services range from basic qualitative tests, such as immunoassay test strips, to comprehensive quantitative analyses, such as mass spectrometry. In general, there is concern that heterogeneity of a drug mixture adds significant uncertainty when using drug checking results based on a small subsamples. The presence of hot spots of active drug components in this context is often termed the 'chocolate chip cookie effect'. Establishing the limitations of the service are essential for interpretation of the results. METHODS: This study assesses the consequence of drug heterogeneity and sampling of consumer level opioid purchased in Victoria, British Columbia ( n = 21 , 50-100 mg each) on quantitative fentanyl results determined from testing with paper spray mass spectrometry. RESULTS: Using descriptive statistics, such as relative standard deviation and interquartile range, the results demonstrate varied distributions of fentanyl concentrations within a single drug batch. However, the presence of hot spots, defined as outliers, were relatively rare. CONCLUSIONS: This study found that the variability in fentanyl concentration from drug heterogeneity and sampling is greater than that attributed to the analytical technique. On a practical level, this provides data to help guide communication of limitations of drug checking services, supporting the aim of trust and transparency between services and people who use drugs. However, if drug checking services continue to be restricted from fully engaging with the reality of manufacturing, buying, selling, mixing and dosing practices, the accuracy, usefulness, and impact will always be limited.

Point-of-care community drug checking technologies: an insider look at the scientific principles and practical considerations.

Drug checking is increasingly being explored outside of festivals and events to be an ongoing service within communities, frequently integrated within responses to illicit drug overdose. The choice of instrumentation is a common question, and the demands on these chemical analytical instruments can be challenging as illicit substances may be more complex and include highly potent ingredients at trace levels. The answer remains nuanced as the instruments themselves are not directly comparable nor are the local demands on the service, meaning implementation factors heavily influence the assessment and effectiveness of instruments. In this perspective, we provide a technical but accessible introduction to the background of a few common drug checking methods aimed at current and potential drug checking service providers. We discuss the following tools that have been used as part of the Vancouver Island Drug Checking Project in Victoria, Canada: immunoassay test strips, attenuated total reflection IR-absorption spectroscopy, Raman spectroscopy from powder samples, surface-enhanced Raman scattering in a solution of colloidal gold nanoparticles, and gas chromatography-mass spectrometry. Using four different drug mixtures received and tested at the service, we illustrate the strengths, limitations, and capabilities of such instruments, and expose the scientific theory to give further insight into their analytical results. Each case study provides a walk-through-style analysis for a practical comparison between data from several different instruments acquired on the same sample. Ideally, a single instrument would be able to achieve all of the objectives of drug checking. However, there is no clear instrument that ticks every box; low cost, portable, rapid, easy-to-use and provides highly sensitive identification and accurate quantification. Multi-instrument approaches to drug checking may be required to effectively respond to increasingly complex and highly potent substances demanding trace level detection and the potential for quantification.

Ions, metabolites, and cells: Water as a reporter of surface conditions during bacterial growth.

Surface-specific nonlinear vibrational spectroscopy, combined with bulk solution measurements and imaging, is used to study the surface conditions during the growth of E. coli. As a result of the silica high surface charge density, the water structure at the silica-aqueous interface is known to be especially sensitive to pH and ionic strength, and surface concentration profiles develop that can be appreciably different from the bulk solution conditions. We illustrate that, in the presence of growing cells, a unique surface micro-environment is established as a result of metabolites accumulating on the silica surface. Even in the subsequent absence of the cells, this surface layer works to reduce the interfacial ionic strength as revealed by the enhanced signal from surface water molecules. In the presence of growing cells, an additional boost in surface water signal is attributed to a local pH that is higher than that of the bulk solution.

Toward automated infrared spectral analysis in community drug checking.

The body of knowledge surrounding infrared spectral analysis of drug mixtures continues to grow alongside the physical expansion of drug checking services. Technicians trained in the analysis of spectroscopic data are essential for reasons that go beyond the accuracy of the analytical results. Significant barriers faced by people who use drugs in engaging with drug checking services include the speed and accuracy of the results, and the availability and accessibility of the service. These barriers can be overcome by the automation of interpretations. A random forest model for the detection of two compounds, MDA and fluorofentanyl, was trained and optimized with drug samples acquired at a community drug checking site. This resulted in a 79% true positive and 100% true negative rate for MDA, and 61% true positive and 97% true negative rate for fluorofentanyl. The trained models were applied to selected drug samples to demonstrate a proposed workflow for interpreting and validating model predictions. The detection of MDA was demonstrated on three mixtures: (1) MDMA and MDA, (2) MDA and dimethylsulfone, and (3) fentanyl, etizolam, and benzocaine. The classification of fluorofentanyl was applied to a drug mixture containing fentanyl, fluorofentanyl, 4-anilino-N-phenethylpiperidine, caffeine, and mannitol. Feature importance was calculated using shapely additive explanations to better explain the model predictions and k-nearest neighbors was used for visual comparison to labelled training data. This is a step toward building appropriate trust in computer-assisted interpretations in order to promote their use in a harm reduction context.

Exploring the use of infrared absorption spectroscopy and two-trace two-dimensional correlation analysis for the resolution of multi-component drug mixtures.

Community drug checking provides an essential service that responds to the unpredictable and variable supply of illicit drugs. Point of care detection of trace components using portable infrared spectrometers is a harm reduction measure to prevent overdose. This study investigates the ability of weighted subtraction and two-trace two-dimensional (2T2D) correlation analysis to reveal the presence of heroin in an opioid mixture that contains heroin and fentanyl mixed with caffeine as a cutting agent. In both methods, a spectral trace was identified that provided reasonably high correlation scores to heroin when compared to entries in drug libraries. The two-trace correlation analysis produced a higher match score, suggesting that future improvements in spectral unmixing methods may enhance the reliability of detecting trace components in drugs.

Rapid and accurate etizolam detection using surface-enhanced Raman spectroscopy for community drug checking.

BACKGROUND: In British Columbia, Canada, illicit opioids have been increasingly combined with etizolam, a benzodiazepine analog, that continues to challenge popular portable drug checking technologies as it is often present in low concentrations as a result of its high potency. An unknown combination of opioids and benzodiazepines may have dangerous consequences due to unpredictable dosing, increased respiratory depression, and complicated overdose response measures. METHODS: Surface-enhanced Raman spectroscopy (SERS) using a portable Raman spectrometer is used to establish a univariate model for the detection of etizolam in opioid drug mixtures (n=100) obtained from the Vancouver Island Drug Checking Project, where the presence of etizolam has been determined using paper-spray mass spectrometry. Benzodiazepine immunoassay test strips are also performed on all samples for comparison. RESULTS: SERS is shown to detect etizolam with high sensitivity (96%) and specificity (86%). In contrast, benzodiazepine test strips demonstrate a low sensitivity (8%) for the detection of etizolam of the same samples (n=100), with only small improvements when studied over a larger subset of samples (n=506, sensitivity = 29%). CONCLUSION: We have demonstrated the potential of SERS for trace detection of etizolam within complex sample matrices. Since SERS is one of the few portable technologies capable of trace detection, further studies on its ability for quantification and discrimination of trace adulterants in street samples is of significant interest for point-of-care applications.

Portable gas chromatography-mass spectrometry in drug checking: Detection of carfentanil and etizolam in expected opioid samples.

BACKGROUND: There has been a recent increase in adulteration of opioids with low concentration actives such as fentanyl analogues and benzodiazepines. As drug checking projects using vibrational spectroscopy continue to seek confirmatory lab-based testing, the concern and reality of missing these potentially harmful substances in point-of-care testing is prevalent. METHODS: A portable GC-MS was used to analyze select opioid samples acquired at a drug checking service in Victoria, Canada (n=59). Certified reference standards of several fentanyl analogues and benzodiazepines were measured to guide targeted analysis of these samples. Results were compared with those obtained using a lab-based paper spray mass spectrometer. RESULTS: Portable GC-MS was able to identify 62% of samples containing carfentanil and 36% of samples containing etizolam. In the case of etizolam, the success rate was higher for more potent samples: 78% of etizolam-containing samples were identified when the etizolam concentration was above 3% by weight. In comparison, infrared spectroscopy was able to detect etizolam in only 9% of the etizolam-containing samples, and is not sensitive enough to detect carfentanil at relevant concentrations. CONCLUSIONS: Portable GC-MS has potential in identifying low concentration substances in a point-of-care setting, without relying on subsequent off-site confirmatory testing.

Implementing an integrated multi-technology platform for drug checking: Social, scientific, and technological considerations.

The illicit drug overdose crisis in North America continues to devastate communities with fentanyl detected in the majority of illicit drug overdose deaths. The COVID-19 pandemic has heightened concerns of even greater unpredictability in the drug supplies and unprecedented rates of overdoses. Portable drug-checking technologies are increasingly being integrated within overdose prevention strategies. These emerging responses are raising new questions about which technologies to pursue and what service models can respond to the current risks and contexts. In what has been referred to as the epicenter of the overdose crisis in Canada, a multi-technology platform for drug checking is being piloted in community settings using a suite of chemical analytical methods to provide real-time harm reduction. These include infrared absorption, Raman scattering, gas chromatography with mass spectrometry, and antibody-based test strips. In this Perspective, we illustrate some advantages and challenges of using multiple techniques for the analysis of the same sample, and provide an example of a data analysis and visualization platform that can unify the presentation of the results and enable deeper analysis of the results. We also highlight the implementation of a various service models that co-exist in a research setting, with particular emphasis on the way that drug checking technicians and harm reduction workers interact with service users. Finally, we provide a description of the challenges associated with data interpretation and the communication of results to a diverse audience.