JOURNAL ARTICLE

A Validated UHPLC–MS/MS Method for Determination of Nalbuphine in Human Plasma and Application for Pharmacokinetic Study of Patients Undergoing General Anesthesia.

  • Published In: Journal of Chromatographic Science, 2023, v. 61, n. 8. P. 758 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Gao, Xiaonan; Nie, Xuyang; Gao, Jinglin; Heng, Tianfang; Zhang, Yuqi; Hua, Li; Sun, Yaqi; Feng, Zhangying; Jia, Li; Wang, Mingxia 3 of 3

Abstract

The article focuses on the development and validation of a rapid, simple, sensitive, and economical ultra-performance liquid chromatography–tandem mass spectrometry (UHPLC–MS/MS) method for quantifying nalbuphine, a semisynthetic opioid analgesic, in human plasma. This method was applied to pharmacokinetic studies in surgical patients undergoing general anesthesia for abdominal surgery, revealing pharmacokinetic parameters such as peak plasma concentration, clearance, and half-life, with observed differences in elimination half-life related to patient age. The study highlights that nalbuphine's pharmacokinetics in surgical patients differ from healthy volunteers, likely due to perioperative physiological changes, and suggests that age-related changes in half-life may not necessitate dose adjustments. The validated UHPLC–MS/MS method offers improved efficiency and sensitivity over previous assays, supporting its use in clinical pharmacokinetic monitoring of nalbuphine.

Additional Information

  • Source:Journal of Chromatographic Science. 2023/09, Vol. 61, Issue 8, p758
  • Document Type:Article
  • Subject Area:Mathematics
  • Publication Date:2023
  • ISSN:0021-9665
  • DOI:10.1093/chromsci/bmac094
  • Accession Number:172443400
  • Copyright Statement:Copyright of Journal of Chromatographic Science 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.