Single-Run RP-HPLC Method for Comprehensive Separation of Nine Bisoprolol Fumarate Impurities Aligned with USP and EP
Abstract
A novel, efficient reverse phase high performance liquid chromatography (RP-HPLC) method was developed and validated for the complete separation of nine impurities related to Bisoprolol fumarate active pharmaceutical ingredient. Since existing pharmacopeial methods have various analytical limitations, addressing only a subset of selected impurities, often require multiple approaches in order to meet regulatory expectations. Method validation was conducted in accordance with USP general chapter <1225> and ICH Q2 (R1) guidelines, ensuring satisfactory specificity, linearity, precision, accuracy, robustness and stability across a concentration range extending from the limit of quantification to 120% of the specified impurity level. Optimum chromatographic conditions including the use of appropriate stationary phase selection employing a monolithic silica rod with octyl silane chemically bonded to superficially porous silica particles (3 μm), buffer composition, temperature, flow rate and gradient elution enabled successful baseline resolution of all targeted impurities within a single analytical run. A very successful and efficient impurity separations were carried out by optimising chromatographic conditions by making use of differences in polarities of impurities chosen. Besides that, the procedures showed some robustness, assuring reliable results, despite deliberate minor alterations in experimental parameters. The developed RP-HPLC method’s ease of operation, its cost effectiveness, compliance with regulations and applicability to a wide variety of pharmaceutical products makes it an attractive technique in pharmaceutical analysis to meet the standards of regulatory acceptance that help in producing quality products.
Citation Information
@article{monikatrivedi2026,
title={Single-Run RP-HPLC Method for Comprehensive Separation of Nine Bisoprolol Fumarate Impurities Aligned with USP and EP},
author={Monika Trivedi},
journal={Research Square},
year={2026},
doi={https://doi.org/10.21203/rs.3.rs-9461723/v1}
}
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