JOURNAL ARTICLE
Validation of an HPLC–HR-MS Method for the Determination and Quantification of Six Drugs (Morphine, Codeine, Methadone, Alprazolam, Clonazepam and Quetiapine) in Nails.
Published In: Journal of Analytical Toxicology, 2023, v. 47, n. 5. P. 488 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Buratti, Erika; Cippitelli, Marta; Mietti, Gianmario; Scendoni, Roberto; Froldi, Rino; Cerioni, Alice; Cingolani, Mariano 3 of 3
Abstract
This article focuses on the development and validation of a sensitive analytical method for the simultaneous extraction and quantification of six drugs—three narcotics (morphine, codeine, methadone), two benzodiazepines (clonazepam, alprazolam), and one antipsychotic (quetiapine)—from human nail matrices using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC–HR-MS). The method, validated according to the Scientific Working Group for Forensic Toxicology (SWGTOX) standards, demonstrated high sensitivity, accuracy, and precision, with successful application to authentic postmortem and living donor nail samples. Nails, as keratinized matrices, offer a non-invasive, stable, and long-term record of substance intake, making them valuable in forensic toxicology, especially when conventional matrices like blood or urine are unavailable or limited. The study highlights the potential of this UHPLC–HR-MS method for forensic investigations, drug monitoring, and occupational exposure assessments.
Additional Information
- Source:Journal of Analytical Toxicology. 2023/06, Vol. 47, Issue 5, p488
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2023
- ISSN:0146-4760
- DOI:10.1093/jat/bkad022
- Accession Number:163826845
- 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.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.