摘要
该研究基于超高效液相色谱-四极杆-飞行时间串联质谱(UPLC-Q-TOF-MS/MS)技术建立了奈玛特韦、利托纳韦和奥司他韦的快速分析方法,对上述3种抗病毒药物进行了二级质谱碎裂机理精细分析。通过机器学习构建的自动解析算法实现了标样非依赖性鉴定,即使在血清1%加标水平下仍有100%检出率,对低丰度碎片离子表现出足够的识别能力。在模型测试中,该系统能有效区分目标化合物与基质干扰物,展现出良好的特异性。采用该方法成功从实际药品中分离鉴定出奈玛特韦、利托纳韦和奥司他韦,匹配得分均高于85。该研究为抗病毒药物质量监控提供了新思路和技术支持。
This study developed a method for rapid analysis of nirmatrelvir,ritonavir and oseltamivir based on ultra-high performance liquid chromatography-quadrupole-time-of-flight tandem mass spectrometry(UPLC-Q-TOF-MS/MS)technique.The MS/MS fragmentation mechanisms for these three antiviral drugs were studied in detail.An automatic interpretation algorithm built by machine learning enables standard-independent identification,with 100%detection rate even at 1%spiked level in serum.It showed sufficient recognition ability for low-abundance fragment ions.The system could effectively distinguish target compounds from matrix interferences.It demonstrated good specificity in the model test.Using this method,nirmatrelvir,ritonavir,and oseltamivir were successfully separated and identified in real drug samples with matching scores all above 85.This study thus provides new ideal and technical support for the quality supervision of antiviral drugs.
作者
李奕晓
陈苏微
周淳
胡坪
肖雪
章弘扬
LI Yi-xiao;CHEN Su-wei;ZHOU Chun;HU Ping;XIAO Xue;ZHANG Hong-yang(School of Chemistry and Molecular Engineering,East China University of Science and Technology,Shanghai 200237,China;Guangdong Metabolic Disease Research Center of Integrated Chinese and Western Medicine(Institute of Traditional Chinese Medicine),Guangdong Pharmaceutical University,Guangzhou 510006,China;NMPA Key Laboratory for Rapid Testing Technology of Drugs(Guangdong Institute for Drug Control),Guangzhou 510663,China;Shanghai Ganhong Biomedical Technology Co.,Ltd.,Shanghai 200131,China)
出处
《分析测试学报》
2025年第12期2462-2469,共8页
Journal of Instrumental Analysis
基金
中国仪器仪表学会科学仪器托举计划项目(CISTJ2024)
国家药品监督管理局药品快速检验技术重点实验室开放课题(KF2022006)。
关键词
抗病毒药物
质量监控
液质联用
裂解机理
机器学习
模型测试
antiviral drugs
quality supervision
LC-MS
fragmentation mechanism
machine learning
model testing
作者简介
通讯作者:肖雪,博士,副研究员,研究方向:中药质量研究、药品快检与过程分析,E-mail:erxiaohappy@163.com;通讯作者:章弘扬,博士,副教授,研究方向:LC-MS复杂体系分析新方法,E-mail:hongyang_zhang@ecust.edu.cn。
