JOURNAL ARTICLE

Chromatographic Methods for the Analysis of the Antipsychotic Drug Clozapine and Its Major Metabolites: A Review.

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

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Hrichi, Hajer; Kouki, Noura; Elkanzi, Nadia Ali Ahmed 3 of 3

Abstract

This article provides a comprehensive review of chromatographic methods used to analyze clozapine (CLZ), a second-generation antipsychotic, and its two main metabolites, norclozapine (NCLZ) and clozapine N-oxide (CLZ-NOX), in pharmaceutical formulations, biological matrices, and environmental samples. It covers various chromatographic techniques including high-performance liquid chromatography (HPLC), liquid chromatography–mass spectrometry (LC–MS), ultra-performance liquid chromatography (UPLC), high-performance thin-layer chromatography (HPTLC), and gas chromatography (GC), highlighting their applications, sample preparation methods, and validation parameters. The review notes that HPLC and LC–MS are the most frequently reported methods, with LC–MS offering superior sensitivity and lower detection limits, though at higher cost and complexity. It also emphasizes the need for improved, automated, and miniaturized sample preparation techniques to enhance efficiency and reduce solvent use, as well as the importance of developing fast and sensitive methods for detecting CLZ in environmental samples. This synthesis aims to assist researchers and clinicians in selecting and optimizing chromatographic methods for therapeutic drug monitoring and environmental analysis of CLZ and its metabolites.

Additional Information

  • Source:Journal of Chromatographic Science. 2025/03, Vol. 63, Issue 3, p1
  • Document Type:Article
  • Subject Area:Complementary and Alternative Medicine
  • Publication Date:2025
  • ISSN:0021-9665
  • DOI:10.1093/chromsci/bmae016
  • Accession Number:184405621
  • 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.)

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