STATISTICAL METHOD OPTIMIZATION AND VALIDATION OF A ROBUST AND EFFICIENT RP-HPLC METHOD FOR SAXAGLIPTIN USING BOX-BEHNKEN DESIGN
This research work aimed to investigate a novel method of Quality by Design (QbD) approach, by applying a Box–Behnken design (BBD) to statistically validate an RP-HPLC method for the estimation of Saxagliptin. The selectedmethod is helpful for structured optimisation by reducing experimental trial and error. This structured optimisationresulted in consistent performance, producing a sharp, symmetrical peak with a short retention time. The methodshowed satisfactory validation results, with system suitability parameters within acceptable limits. It exhibitedexcellent precision (%RSD 0.07%) and good accuracy, with a mean recovery of around 99.50%. Sensitivitywasalso adequate, with LOD and LOQ values of 0.23 µg/mL and 0.71 µg/mL, respectively. These findings confirmthat the method is reliable for routine quantitative analysis. Validation of this method, carried out in compliance withICH Q8(R2) guidelines, supports its suitability for regulatory and quality control applications. Froma broader perspective, this work emphasises the importance of incorporating QbD principles into analytical methoddevelopment. Such an approach improves method robustness, reproducibility, and overall understanding, whilesupporting efficient lifecycle management. The strategy presented in this study can also be adapted for other pharmaceutical compounds, offering a systematic and cost-effective pathway for method development. Overall, thestudy contributes to analytical research by demonstrating how statistical optimisation techniques can improvemethod performance, ensure regulatory compliance, and support reliable pharmaceutical analysis in both academicand industrial applications
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