A study of impurities in the repurposed COVID‐19 drug hydroxychloroquine sulfate using ultra‐high‐performance liquid chromatography‐quadrupole/time‐of‐flight mass spectrometry and liquid chromatography‐solid‐phase extraction‐nuclear magnetic resonance
Donghai Xu, Fangfang Pan, Hao Ruan, Nan Sun
National Medical Products Administration Zhejiang University of Technology
阅读操作
确认中在文库中上传 PDF 后可生成中文音频讲解。
摘要与影响
Rationale Hydroxychloroquine sulfate is effective in the treatment of malaria and autoimmune diseases and as an antiviral drug. However, unreported impurities are often detected in this drug, which pose a health risk. In this study, the structures of hydroxychloroquine and six unknown impurities were analyzed using ultra‐high‐performance liquid chromatography‐quadrupole/time‐of‐flight‐tandem mass spectrometry (UHPLCQ/TOF/MS/MS), and the structures were characterized using liquid chromatography‐solid‐phase extraction‐nuclear magnetic resonance (LC‐SPE‐NMR) spectroscopy. Methods An Agilent InfinityLad Poroshell HPH‐C18 column (100 × 4.6 mm, 2.7 μm) was used. For the analysis of hydroxychloroquine and six unknown impurities, the mobile phase was 20 mM ammonium formate aqueous solution and methanol/acetonitrile (80:20, v/v) using gradient elution. Full‐scan MS and MS 2 were performed to obtain as much structural information as possible. In addition, six unknown impurities were separated by semipreparative liquid chromatography and characterized using LC‐SPE‐NMR. Results The MS 2 fragmentation patterns of the impurities were investigated, leading to more structural information and an understanding of the fragmentation pathways of the impurities. The structures of the unknown impurities were confirmed using NMR. In addition, some possible pathways of the formation of the impurities in the drugs were outlined, and these impurities were found to be process impurities. Conclusions Based on the identification and characterization of these impurities, this study also describes the cause of the production of the impurities and provides insights for companies to improve their production processes and a scientific basis for the improvement of the related pharmacopoeias.
逐年被引趋势
关键指标
同类平均 = 1
同领域 · 同年份 · 同类型
Google Scholar 与 OpenAlex 的被引统计范围不同,数值存在差异属正常。
AI 辅助阅读
依据:摘要
可就本文提问;依据不足时会说明。
学术脉络
学科主题
化学Analytical Methods in Pharmaceuticals
Pesticide Residue Analysis and Safety · Pharmaceutical Quality and Counterfeiting
参考文献 27
此处列出前 3 条
引用本文 4
按被引量排序,此处列出前 3 条