JOURNAL ARTICLE

Green and Sustainable Analytical Chemistry-Driven Chromatographic Method Development for Stability Study of Apixaban Using Box–Behnken Design and Principal Component Analysis.

  • Published In: Journal of Chromatographic Science, 2024, v. 62, n. 7. P. 649 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Prajapati, Pintu; Rajpurohit, Pooja; Pulusu, Veera Shakar; Shah, Shailesh 3 of 3

Abstract

This article focuses on the development of a green and sustainable stability-indicating high-performance thin-layer chromatography (HPTLC) method for the estimation of apixaban (APX), a USFDA-approved anticoagulant drug. The method was designed using the Analytical Quality by Design (AQbD) approach, incorporating chemometric tools such as principal component analysis (PCA) and design of experiments (DoE) to optimize critical method variables while minimizing the use of toxic organic solvents. Unlike previously published chromatographic methods that rely on hazardous solvents like acetonitrile and methanol, this method employs safer class 3 solvents—n-butanol, ethanol, and glacial acetic acid—aligned with International Council for Harmonization (ICH) guidelines, thereby enhancing analyst safety and environmental protection. Validation studies confirmed the method’s specificity, accuracy, precision, robustness, and sensitivity, and forced degradation tests demonstrated its stability-indicating capability. The developed method showed a superior greenness profile compared to existing methods, making it a more eco-friendly and economical option for quality control and stability studies of apixaban pharmaceutical formulations.

Additional Information

  • Source:Journal of Chromatographic Science. 2024/08, Vol. 62, Issue 7, p649
  • Document Type:Article
  • Subject Area:Chemistry
  • Publication Date:2024
  • ISSN:0021-9665
  • DOI:10.1093/chromsci/bmad033
  • Accession Number:179110975
  • 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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