Multivariate Chemometric Models and Application of Genetic Algorithm for Simultaneous Determination of Ledipasvir and Sofosbuvir in Pure Form and in Pharmaceutical Preparation; A Comparative Study

Abstract

Objectives: Four multivariate chemometric methods have been developed for simultaneous determination of sofosbuvir and ledipasvir in their pure and pharmaceutical dosage forms. Methods: Firstly, partial least squares and artificial neural network have been applied for the quantitative analysis of the studied drugs. Results: Experimental design of different synthetic mixtures of sofosbuvir and ledipasvir in different ratios has been done. The zero-order absorption spectra of these prepared mixtures have been recorded over the wavelength range 200-400 nm with 1 nm interval. The obtained absorbance and concentration data matrix have been utilized to obtain calibration or regression analysis data which has been used for the prediction of the unknown concentrations of each drug in their mixtures. Alternatively, the application of genetic algorithm to partial least squares and artificial neural network has been done and greatly increased the precision and predictive ability of the methods. The four methods have been successfully applied to quantify sofosbuvir and ledipasvir in the real market sample. Conclusion: The investigated methods have been found to be accurate, precise and could resolve the overlapped spectra of the mixture without any preliminary separation steps.