Simultaneous spectrophotometric quantitative analysis of elbasvir and grazoprevir using assisted chemometric models.

ABSTRACT

Artificial neural networks and genetic algorithm artificial neural networks, chemometric assisted spectrophotometric models, were developed for the quantitative analysis of elbasvir and grazoprevir in their newly FDA approved pharmaceutical dosage form. The UV absorption spectra of elbasvir and grazoprevir show severe degree of overlap which caused difficulty for selecting certain spectrophotometric method with advantage of simultaneous quantitative analysis of the cited drugs. After extensive study and many experimental trials, artificial neural networks and genetic algorithm artificial neural networks were the suitable models for the quantitative analysis of studied drugs in their binary mixture. Experimental design and constructing the calibration and validation sets of the binary mixture were achieved to implement the proposed models. The models were optimized with the aid of five-levels, two factors experimental design. The designed models were successfully applied to the quantitative analysis of Zepatier® tablets. The results were statistically compared with another reported HPLC quantitative analytical method with no significant difference by applying Student t-test and variance ratio F-test.