Application of Chromatographic Response Function in Development of Stability Indicating HPLC Method for Determination of Benoxinate Hydrochloride and Fluorescein Sodium Mixture Using Factorial Design

Document Type : Original Article

Authors

1 Department of Pharmaceutical Analytical Chemistry, Faculty of Pharmacy, Tanta University, Tanta, Egypt

2 Department of pharmaceutical analytical Chemistry, Faculty of Pharmacy, Tanta University, Tanta, Egypt

Abstract

A simple and rapid stability-indicating RP-HPLC method was developed and validated for quantitative determination of benoxinate hydrochloride and fluorescein sodium binary mixture. Both drugs were subjected to different stress conditions including hydrolysis under acidic and alkaline conditions, and oxidation by hydrogen peroxide. The optimization of forced degradation conditions was done by application of experimental factorial design, which helped to enrich levels of degradation products. Different chromatographic response functions were tried to find out the best function that reflects the overall quality of the chromatogram. NCRF was found to be the optimum function, so it was selected as a response to be optimized in factorial design that was implemented to find the optimum chromatographic conditions. The chromatographic conditions obtained from factorial design led to the use of mobile phase consisting of a mixture of 40% acetonitrile and 60% 50 mM potassium dihydrogen phosphate buffer containing 0.01% triethylamine (pH adjusted to 5.0) at a flow rate of 1.5 mL/min and column temperature kept at 40ºC. Inertsil ODS-3(250 mm x 4.6 mm, 5μm) column was used as stationary phase and the detection was performed at 220 nm using PDA detector. The HPLC method was successfully applied to the determination of benoxinate hydrochloride and fluorescein sodium in synthetic mixture, and the percent recovery ± standard deviation (SD) was 100.56 ± 0.59 and 98.36 ± 0.29 for benoxinate hydrochloride and fluorescein sodium respectively. The method was found to be simple and rapid with less trial and error experiments by applying factorial design.

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