JOURNAL ARTICLE

Chromatographic Separation and Quantification of Nine Nitrosamine Genotoxic Impurities in a Single Method in Nebivolol Tablet by Using Validated Ultra-Sensitive Liquid Chromatography: Mass Spectrometry Analytical Method.

  • Published In: Journal of Chromatographic Science, 2025, v. 63, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Pathak, Mehul; Patel, Dhara D; Kumar, Dalip; Singh, Avineesh; Agrawal, Suresh 3 of 3

Abstract

This article focuses on the development and validation of a sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method with atmospheric pressure chemical ionization (APCI) for simultaneous detection and quantification of nine nitrosamine impurities in nebivolol tablets. Nitrosamines, classified as Class 1 genotoxic impurities by the International Council for Harmonization (ICH), pose carcinogenic risks even at trace levels, necessitating highly sensitive analytical methods for pharmaceutical quality control. The validated method demonstrated specificity, accuracy, precision, linearity (r² > 0.99), and low limits of quantification (approximately 10–20 parts per billion), enabling reliable detection of nitrosamines well below regulatory limits. Application of the method to commercial nebivolol samples showed no detectable nitrosamine impurities, supporting its suitability for routine monitoring to ensure drug safety and compliance with regulatory standards.

Additional Information

  • Source:Journal of Chromatographic Science. 2025/01, Vol. 63, Issue 1, p1
  • Document Type:Article
  • Subject Area:Chemistry
  • Publication Date:2025
  • ISSN:0021-9665
  • DOI:10.1093/chromsci/bmae061
  • Accession Number:182369634
  • 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.