Understanding sensitivity and cross-reactivity of xylazine lateral flow immunoassay test strips for drug checking applications.

drug checking fentanyl harm reduction test strips xylazine

Journal

Drug testing and analysis
ISSN: 1942-7611
Titre abrégé: Drug Test Anal
Pays: England
ID NLM: 101483449

Informations de publication

Date de publication:
03 Dec 2023
Historique:
revised: 03 11 2023
received: 01 09 2023
accepted: 06 11 2023
medline: 4 12 2023
pubmed: 4 12 2023
entrez: 3 12 2023
Statut: aheadofprint

Résumé

The continued prevalence of xylazine in the illicit drug market has necessitated development of quick and simple methods for identification, including lateral flow immunoassays (also known as "test strips"), like those frequently used to detect fentanyl. This study explored the drug checking applicability of the first publicly available xylazine test strips (XTS) using four sub-studies: reproducibility (i.e., consistency of positive results in a highly-concentrated xylazine solution); limit of detection on a calibration curve of xylazine concentrations; cross-reactivity against 77 commonly encountered drugs, cutting agents, and other structurally similar compounds; and applicability for analyzing community-acquired samples-where 100 drug residue samples were analyzed using XTS, direct analysis in real time mass spectrometry (DART-MS), and gas chromatography tandem mass spectrometry (GC-MS/MS). XTS consistently detected xylazine at concentrations ≥2.5 μg/ml, and XTS results were reproducible. Sensitivity and specificity of XTS were calculated by comparing expected versus obtained results based on xylazine concentration of community-acquired samples measured by GC-MS/MS. XTS consistently detected xylazine in samples with concentration >2 μg/ml and yielded a sensitivity of 0.974, specificity of 1.00, and overall accuracy of 0.986. Cross-reactivity with lidocaine, a common cutting agent, and lack of XTS reactivity with other α

Identifiants

pubmed: 38043944
doi: 10.1002/dta.3612
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2023 John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Références

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Auteurs

Edward Sisco (E)

National Institute of Standards and Technology, Gaithersburg, Maryland, USA.

Danielle F Nestadt (DF)

Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

Madeline B Bloom (MB)

Department of Forensic Sciences, George Washington University, Washington, DC, USA.

Kristin E Schneider (KE)

Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

Rae A Elkasabany (RA)

Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

Saba Rouhani (S)

Department of Epidemiology, New York University School of Global Public Health, New York, New York, USA.

Susan G Sherman (SG)

Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

Classifications MeSH