Chemometric-assisted spectrophotometric approach for stability assessment of safinamide and its synthetic precursor in antiparkinsonian formulation with sustainability profiling

Parkinson's disease (PD) is a significant health issue that affects older individuals. Safinamide mesylate (SAF) is a recently developed adjunct therapy increasingly employed in the management of PD. Ensuring the stability of new drug formulations and establishing suitable stability-indicating methodologies is crucial for pharmaceutical analysis. The current study has developed three new, user-friendly, and robust mathematical methods using multivariate spectrophotometric analysis to quantify SAF, its synthetic precursor impurity (4-HBD), and stress-induced degradation products. The multivariate algorithms used include principal component regression (PCR), partial least squares (PLS), and synergy intervals partial least squares (siPLS). The ranges of the proposed methods were 3.00-23.00, 1.00-5.00, 2.50-6.50, and 5.00-13.00 µg/mL for SAF, 4-HBD, SAF hydrolytic degradation products, and SAF oxidative degradation products, respectively. When analyzed using the applied methods, the commercially available tablet preparation showed stability with no impurities or interference from tablet additives. In terms of accuracy and precision, the statistical analysis also revealed no significant differences in comparison to the reported methods. The developed multivariate models were validated using internal and external validation sets. The results demonstrated that the siPLS model outperformed PCR and PLS based on the root mean square error of prediction (RMSEP) and the correlation coefficient values (r). Furthermore, these methods can be used as a substitute for HPLC in quality control laboratories when multiple samples need to be analyzed within a short timeframe. Finally, various sustainability assessment tools were utilized to evaluate and measure the environmental background of the established methods.