JOURNAL ARTICLE
N,N-Dimethylpentylone (dipentylone)—A new synthetic cathinone identified in a postmortem forensic toxicology case series.
Published In: Journal of Analytical Toxicology, 2023, n. 8. P. 753 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Fogarty, Melissa F; Krotulski, Alex J; Papsun, Donna M; Walton, Sara E; Lamb, Michael; Truver, Michael T; Chronister, Chris W; Goldberger, Bruce A; Logan, Barry K 3 of 3
Abstract
This article focuses on the emergence, detection, and forensic significance of N,N-dimethylpentylone, a synthetic cathinone belonging to the beta-keto methylenedioxyamphetamine subclass of novel psychoactive substances (NPS). A novel liquid chromatography–triple quadrupole mass spectrometry (LC–QQQ-MS) method using standard addition was developed and validated to quantitatively confirm N,N-dimethylpentylone, its metabolite pentylone, and eutylone in postmortem cases. Analysis of 18 forensic cases from the United States revealed blood concentrations of N,N-dimethylpentylone ranging from 3.3 to 970 ng/mL, often in combination with other drugs such as fentanyl and methamphetamine, with several deaths attributed primarily to N,N-dimethylpentylone intoxication. Given its rising prevalence and structural similarity to other isomers, the study emphasizes the importance of analytical methods capable of distinguishing N,N-dimethylpentylone from related compounds and recommends its inclusion in routine forensic toxicology screening.
Additional Information
- Source:Journal of Analytical Toxicology. 2023/10, Issue 8, p753
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2023
- ISSN:0146-4760
- DOI:10.1093/jat/bkad037
- Accession Number:173369940
- 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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