Abstract
The widespread clinical utilization of amlodipine-aspirin combinations, despite potential pharmacodynamic interactions and the high prevalence of drug-drug interactions in cardiovascular patients, necessitates robust analytical methods for pharmaceutical quality control and therapeutic drug monitoring. Current analytical approaches face limitations including lengthy analysis times, substantial solvent consumption, and high operational costs. This study presents a novel spectrofluorimetric method coupled with genetic algorithm-enhanced partial least squares (GA-PLS) regression for simultaneous quantification of amlodipine and aspirin in pharmaceutical formulations and biological plasma samples. Synchronous fluorescence spectroscopy at Δλ = 100 nm in 1% sodium dodecyl sulfate-ethanolic medium enhanced spectral characteristics, while chemometric approaches were essential to address remaining spectral overlap for accurate quantification. The GA-PLS approach demonstrated superior performance over conventional partial least squares regression, achieving relative root mean square errors of prediction (RRMSEP) of 0.93 and 1.24 for amlodipine and aspirin respectively, with limits of detection of 22.05 and 15.15 ng/mL. Genetic algorithm optimization reduced spectral variables to approximately 10% of the original dataset while maintaining optimal model performance with only two latent variables. Method validation according to ICH Q2(R2) guidelines demonstrated excellent accuracy (98.62-101.90% recovery) and precision (RSD < 2%) across the analytical range of 200-800 ng/mL. Statistical comparison with established HPLC reference methods showed no significant differences, while application in human plasma achieved recoveries of 95.58-104.51% with coefficient of variation below 5%. Multi-dimensional sustainability assessment using the MA Tool and RGB12 whiteness evaluation achieved an overall score of 91.2%, demonstrating clear superiority over conventional HPLC-UV (83.0%) and LC-MS/MS (69.2%) methods across environmental, analytical, and practical dimensions. The developed method provides a sustainable, cost-effective alternative for routine pharmaceutical analysis, demonstrating enhanced performance through intelligent variable selection and improved operational efficiency.