Testing for protonitazene in human hair using LC–MS-MS

芬太尼 色谱法 检出限 梯度洗脱 化学 洗脱 液相色谱-质谱法 质谱法 止痛药 头发分析 药理学 医学 高效液相色谱法 替代医学 病理
作者
Pascal Kintz,Alice Ameline,Laurie Gheddar,Simona Pichini,Cédric Mazoyer,Katy Teston,Frédéric Aknouche,Christophe Maruéjouls
出处
期刊:Journal of Analytical Toxicology [Oxford University Press]
卷期号:48 (8): 630-635 被引量:2
标识
DOI:10.1093/jat/bkae050
摘要

Abstract Protonitazene is a synthetic benzimidazole opioid of the nitazenes class, developed in the 1950s as an effective analgesic, but never released on the market due to severe side effects and possible dependence. Despite its increasing use as a new psychoactive substance starting in 2019, its detection in human hair of intoxicated and deceased consumers has never been reported. We present the development and validation of a specific procedure to identify protonitazene in hair by liquid chromatography with tandem mass spectrometry. Drugs were incubated overnight at 40°C in 1 mL borate buffer, pH 9.5 with 20 mg pulverized hair and 1 ng/mg fentanyl-d5 used as internal standard. Drugs were then extracted with a mixture of organic solvents. The chromatographic separation was performed using an HSS C18 column with a 15-min gradient elution. Linearity was verified from 1 to 100 pg/mg. The limit of detection was estimated at 0.1 pg/mg. No interference was noted from a large panel of natural and synthetic opioids, fentanyl derivatives, or other new synthetic opioids. Protonitazene was identified at 70 and >7600 pg/mg in the whole head hair specimens of two male subjects deceased from an acute drug overdose in jail. Protonitazene was also identified at 14 and 54 pg/mg in two living co-prisoners. As nitazenes represent a growing threat to public health in various parts of the world, this method was developed in response to the challenges posed by the identification of this class of substances.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
研友_VZG7GZ应助wj采纳,获得10
刚刚
SNOWSUMMER发布了新的文献求助10
刚刚
1秒前
zzjjss完成签到,获得积分10
1秒前
ssy发布了新的文献求助10
1秒前
贝贝发布了新的文献求助10
1秒前
1秒前
2秒前
zhengts完成签到 ,获得积分10
2秒前
rong发布了新的文献求助10
2秒前
Wenge发布了新的文献求助10
3秒前
无花果应助鳗鱼宛凝采纳,获得10
3秒前
3秒前
田様应助spongxin采纳,获得10
4秒前
共享精神应助Zhang采纳,获得10
4秒前
5秒前
5秒前
科研123完成签到,获得积分20
5秒前
ZcLee完成签到,获得积分10
6秒前
6秒前
赘婿应助bin采纳,获得10
6秒前
jiangzong完成签到,获得积分10
6秒前
刘乐乐发布了新的文献求助10
6秒前
7秒前
001发布了新的文献求助10
7秒前
溟曦完成签到,获得积分10
7秒前
天天快乐应助Dog采纳,获得10
7秒前
7秒前
曹飒丽完成签到 ,获得积分10
8秒前
在水一方应助vds采纳,获得10
9秒前
沐辰发布了新的文献求助10
9秒前
沉默凡之完成签到,获得积分10
9秒前
yi5feng发布了新的文献求助10
9秒前
LUKETY发布了新的文献求助10
9秒前
9秒前
LEE发布了新的文献求助20
9秒前
小蘑菇应助zijia采纳,获得30
9秒前
jiangzong发布了新的文献求助10
10秒前
10秒前
10秒前
高分求助中
Markov Chain Monte Carlo 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
Advanced Weaponeering Fourth Edition, Volume 2 1000
悉尼大学博士学位论文,题目:Modelling and testing of one-sided stitched laminated composites. 作者:Kristopher P. Plain 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
Data book on fatigue strength of metallic materials 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7562061
求助须知:如何正确求助?哪些是违规求助? 9142827
关于积分的说明 19547288
捐赠科研通 7150094
什么是DOI,文献DOI怎么找? 3262093
关于科研通互助平台的介绍 2428443
邀请新用户注册赠送积分活动 2251532