An urgent need for community lot testing of lateral flow fentanyl test strips marketed for harm reduction in Northern America.

BACKGROUND: Fentanyl test strips (FTS) are lateral flow immunoassay strips designed for detection of ng/mL levels of fentanyl in urine. In 2021, the US Centers for Disease Control and the Substance Abuse and Mental Health Administration stated that federal funds could be used for procurement of FTS for harm reduction strategies approved by the government such as drug checking. The market for FTS has expanded rapidly in the US and Canada. However, there is no regulatory oversight by either government to ensure proper function of FTS that are being marketed for drug checking. MAIN BODY: Many brands of FTS have rapidly entered the harm reduction market, creating concerns about the reproducibility and accuracy of their performance from brand to brand and lot to lot. Some examples are provided in this Comment. Similar problems with product quality were observed in the mid 2000's when lateral flow immunoassays for malaria were funded in many countries and again in 2020, when COVID-19 tests were in huge demand. The combination of high demand and low levels of regulation and enforcement led some manufacturers to join the goldrush without adequate field testing or quality assurance. We argue that the harm reduction community urgently needs to set a lot checking program in place. A set of simple protocols for conducting the tests and communicating the results have been developed, and are described in the following Perspectives paper in this issue. CONCLUSION: In the absence of governmental regulation and enforcement, the harm reduction community should implement a FTS lot checking program. Based on previous experience with the malaria diagnostic lot checking program, this inexpensive effort could identify products that are not suitable for harm reduction applications and provide valuable feedback to manufacturers. Dissemination of the results will help harm reduction organizations to ensure that FTS they use for drug checking are fit for the purpose.

Enhancing drug checking services for supply monitoring: perspectives on implementation in syringe service programs in the USA.

BACKGROUND: Shifts in the US drug supply, including the proliferation of synthetic opioids and emergence of xylazine, have contributed to the worsening toll of the overdose epidemic. Drug checking services offer a critical intervention to promote agency among people who use drugs (PWUD) to reduce overdose risk. Current drug checking methods can be enhanced to contribute to supply-level monitoring in the USA, overcoming the selection bias associated with existing supply monitoring efforts and informing public health interventions. METHODS: As a group of analytical chemists, public health researchers, evaluators, and harm reductionists, we used a semi-structured guide to facilitate discussion of four different approaches for syringe service programs (SSPs) to offer drug checking services for supply-level monitoring. Using thematic analysis, we identified four key principles that SSPs should consider when implementing drug checking programs. RESULTS: A number of analytical methods exist for drug checking to contribute to supply-level monitoring. While there is likely not a one-size-fits-all approach, SSPs should prioritize methods that can (1) provide immediate utility to PWUD, (2) integrate seamlessly into existing workflows, (3) balance individual- and population-level data needs, and (4) attend to legal concerns for implementation and dissemination. CONCLUSIONS: Enhancing drug checking methods for supply-level monitoring has the potential to detect emerging threats in the drug supply and reduce the toll of the worsening overdose epidemic.

Mitigating the impact of gelatin capsule variability on detection of substandard and falsified pharmaceuticals with near-IR spectroscopy.

Portable NIR spectrometers are effective in detecting authentic pharmaceutical products in intact capsule formulations, which can be used to screen for substandard or falsified versions of those authentic products. However, the chemometric models are trained on libraries of authentic products, and are generally unreliable for detection of quality problems in products from outside their training set, even for products that are nominally the same active pharmaceutical ingredient and same dosage as products in the training set. As part of our research directed at developing better non-brand-specific strategies for pharmaceutical screening, we investigated the impact of capsule composition on NIR modeling. We found that capsule features like gelatin type, color, or thickness, give rise to a similar amount of variance in the NIR spectra as the type of API stored within the capsules. Our results highlight the efficacy of orthogonal projection to latent structures in mitigating the impacts of different types of capsules on the accuracy of NIR chemometric models for classification and regression analysis of lab-made samples. The models showed good performance for classification of field-collected doxycycline capsules as good or bad quality when an NIR-based % w/w metric was used, identifying five samples that were adulterated with talc. However, the % w/w was systematically underestimated, so when evaluating the capsules based on their absolute API content according to the monograph standard, the classification accuracy decreased from 100% to 70%. The underestimation was attributed to an unforeseen variability in the quantities and types of excipients present in the capsules.

Field assessment of active ingredient quantity in pharmaceutical tablets with limited calibration of near infrared spectra: An application to ciprofloxacin tablets.

Portable near-infrared (NIR) spectrophotometers have emerged as valuable tools for identifying substandard and falsified pharmaceuticals (SFPs). Integration of these devices with chemometric and machine learning models enhances their ability to provide quantitative chemical insights. However, different NIR spectrophotometer models vary in resolution, sensitivity, and responses to environmental factors such as temperature and humidity, necessitating instrument-specific libraries that hinder the wider adoption of NIR technology. This study addresses these challenges and seeks to establish a robust approach to promote the use of NIR technology in post-market pharmaceutical analysis. We developed support vector machine and partial least squares regression models based on binary mixtures of lab-made ciprofloxacin and microcrystalline cellulose, then applied the models to ciprofloxacin dosage forms that were assayed with high performance liquid chromatography (HPLC). A receiver operating characteristic (ROC) analysis was performed to set spectrophotometer independent NIR metrics to evaluate ciprofloxacin dosage forms as "meets standard," "needs HPLC assay," or "fails standard." Over 200 ciprofloxacin tablets representing 50 different brands were evaluated using spectra acquired from three types of NIR spectrophotometer with 85% of the prediction agreeing with HPLC testing. This study shows that non-brand-specific predictive models can be applied across multiple spectrophotometers for rapid screening of the conformity of pharmaceutical active ingredients to regulatory standard.