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Abstract BackgroundFentanyl test strips (FTS) are a commonly deployed tool in drug checking, used to test for the presence of fentanyl in street drug samples prior to consumption. Previous reports indicate that in addition to fentanyl, FTS can also detect fentanyl analogs like acetyl fentanyl and butyryl fentanyl, with conflicting reports on their ability to detect fentanyl analogs like Carfentanil and furanyl fentanyl. Yet with hundreds of known fentanyl analogs, there has been no large-scale study rationalizing FTS reactivity to different fentanyl analogs. MethodsIn this study, 251 synthetic opioids—including 214 fentanyl analogs—were screened on two brands of fentanyl test strips to (1) assess the differences in the ability of two brands of fentanyl test strips to detect fentanyl-related compounds and (2) determine which moieties in fentanyl analog chemical structures are most crucial for FTS detection. Two FTS brands were assessed in this study: BTNX Rapid Response and WHPM DanceSafe. ResultsOf 251 screened compounds assessed, 121 compounds were detectable at or below 20,000 ng/mL by both BTNX and DanceSafe FTS, 50 were not detectable by either brand, and 80 were detectable by one brand but not the other (n = 52 BTNX,n = 28 DanceSafe). A structural analysis of fentanyl analogs screened revealed that in general, bulky modifications to the phenethyl moiety inhibit detection by BTNX FTS while bulky modifications to the carbonyl moiety inhibit detection by DanceSafe FTS. ConclusionsThe different “blind spots” are caused by different haptens used to elicit the antibodies for these different strips. By utilizing both brands of FTS in routine drug checking, users could increase the chances of detecting fentanyl analogs in the “blind spot” of one brand. 

Abstract BackgroundFentanyl 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 bodyMany 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. ConclusionIn 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. 

ABSTRACT: Near-infrared (NIR) spectroscopy is a promising technique for field identification of substandard and falsified drugs because it is portable, rapid, nondestructive, and can differentiate many formulated pharmaceutical products. Portable NIR spectrometers rely heavily on chemometric analyses based on libraries of NIR spectra from authentic pharmaceutical samples. However, it is difficult to build comprehensive product libraries in many low- and middle-income countries due to the large numbers of manufacturers who supply these markets, frequent unreported changes in materials sourcing and product formulation by the manufacturers, and general lack of cooperation in providing authentic samples. In this work, we show that a simple library of lab-formulated binary mixtures of an active pharmaceutical ingredient (API) with two diluents gave good performance on field screening tasks, such as discriminating substandard and falsified formulations of the API. Six data analysis models, including principal component analysis and supportvector machine classification and regression methods and convolutional neural networks, were trained on binary mixtures of acetaminophen with either lactose or ascorbic acid. While the models all performed strongly in cross-validation (on formulations similar to their training set), they individually showed poor robustness for formulations outside the training set. However, a predictive algorithm based on the six models, trained only on binary samples, accurately predicts whether the correct amount of acetaminophen is present in ternary mixtures, genuine acetaminophen formulations, adulterated acetaminophen formulations, and falsified formulations containing substitute APIs. This data analytics approach may extend the utility of NIR spectrometers for analysis of pharmaceuticals in low-resource settings. 

Substandard and falsified (SF) pharmaceuticals account for an estimated 10% of the pharmaceutical supply chain in low- and middle-income countries (LMICs), where a lack of regulatory and laboratory resources limits the ability to conduct effective post-market surveillance and allows SF products to penetrate the supply chain. The Distributed Pharmaceutical Analysis Laboratory (DPAL) was established in 2014 to expand testing of pharmaceutical dosage forms sourced from LMICs; DPAL is an alliance of academic institutions throughout the United States and abroad that provides high quality, validated chemical analysis of pharmaceutical dosage forms sourced from partners in LMICs. Results from analysis are reported to relevant regulatory agencies and are used to inform purchasing decisions made by in-country stakeholders. As the DPAL program has expanded to testing more than 1000 pharmaceutical dosage forms annually, challenges have surfaced regarding data management and sample tracking. Here, we describe a pilot project between DPAL and ARTiFACTs that applies blockchain to organize and manage key data generated during the DPAL workflow, including a sample’s progress through the workflow, its physical location, provenance of metadata, and lab reputability. Recording time and date stamps with this data will create a permanent and verifiable chain-of-custody for samples. This secure, distributed ledger will be linked to an easy-to-use dashboard, allowing stakeholders to view results and experimental details for each sample in real time and verify the integrity of DPAL analysis data. Introducing this blockchain-based system as a pilot will allow us to test the technology with real users analyzing real samples. Feedback from users will be recorded and necessary adjustments will be made to the system before the implementation of blockchain across all DPAL sites. Anticipated benefits of implementing blockchain for managing DPAL data include efficient management for routing work, increasing throughput, creating a chain of custody for samples and their data in alignment with the distributed nature of DPAL, and using the analysis results to detect patterns of quality within and across brands of products and develop enhanced sampling techniques and best practices.