JOURNAL ARTICLE

Green TLC-Densitometric Method for Simultaneous Determination of Antazoline and Tetryzoline: Application to Pharmaceutical Formulation and Rabbit Aqueous Humor.

  • Published In: Journal of Chromatographic Science, 2024, v. 62, n. 9. P. 807 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Hussein, Ola G; Rostom, Yasmin; Abdelkawy, Mohamed; Rezk, Mamdouh R; Ahmed, Dina A 3 of 3

Abstract

The article focuses on the development and validation of a novel, green thin-layer chromatographic (TLC) densitometric method for simultaneous determination of antazoline (ANT) and tetryzoline (TET), two ophthalmic drugs commonly used in allergic conjunctivitis treatment. Using silica gel plates and an ethyl acetate:ethanol (5:5, by volume) mobile phase, the method achieves effective separation and quantification of ANT and TET in pure forms, pharmaceutical formulations (Trillerg® sterile ophthalmic solution), and spiked rabbit aqueous humor samples. The method demonstrated high accuracy, precision, and robustness, with statistical analysis showing no significant difference compared to official pharmacopeial methods. Additionally, the environmental impact of the method was assessed using four greenness metrics—analytical greenness (AGREE), green analytical procedure index (GAPI), analytical eco-scale, and national environmental method index (NEMI)—confirming its eco-friendly profile relative to existing methods.

Additional Information

  • Source:Journal of Chromatographic Science. 2024/10, Vol. 62, Issue 9, p807
  • Document Type:Article
  • Subject Area:Chemistry
  • Publication Date:2024
  • ISSN:0021-9665
  • DOI:10.1093/chromsci/bmad042
  • Accession Number:180625811
  • 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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