JOURNAL ARTICLE
A fully validated LC–QTOF-MS screening workflow for the analysis of drugs in oral fluid.
Published In: Journal of Analytical Toxicology, 2025, v. 49, n. 4. P. 265 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Coulter, Cynthia; Gonzales, Jonah; Coulter, Campbell A; Wagner, Jarrad; Moore, Christine 3 of 3
Abstract
This article focuses on the development and validation of a liquid chromatography–quadrupole time of flight tandem mass spectrometry (LC–QTOF-MS) method for screening multiple drugs in oral fluid collected with the Quantisal™ device, targeting compounds relevant to driving under the influence of drugs (DUID) investigations. The method meets or exceeds the National Safety Council's Alcohol, Drugs, and Impairment Division (NSC-ADID) recommended cut-off concentrations for both Tier 1 and Tier 2 drug classes, including novel psychoactive substances and Δ9-tetrahydrocannabinol (THC), using a single liquid–liquid extraction and a 10-minute chromatographic run. Validation followed ANSI/ASB forensic toxicology standards, demonstrating acceptable limits of detection, stability, interference, and matrix effects, with successful application to proficiency and authentic oral fluid samples. The LC–QTOF-MS approach offers advantages over traditional immunoassay screening by enabling broad-spectrum, retrospective data analysis without the need for immunoassay testing, although it currently excludes negatively ionized compounds such as barbiturates. This method represents the first validated LC–QTOF-MS screening procedure aligned with NSC-ADID recommendations for oral fluid in forensic toxicology.
Additional Information
- Source:Journal of Analytical Toxicology. 2025/05, Vol. 49, Issue 4, p265
- Document Type:Article
- Subject Area:Chemistry
- Publication Date:2025
- ISSN:0146-4760
- DOI:10.1093/jat/bkaf013
- Accession Number:185321118
- Copyright Statement:Copyright of Journal of Analytical Toxicology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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