Multivariable Manipulation of Spectrophotometric Data with Genetic Algorithm Selection for Novel Quantitative Resolution of Five Antiviral Drugs in Their Pharmaceutical Products

Abstract

Background: In many real-world situations there are many components in a mixture that produce an enormous amount of information. Objective: The main task is to build up balanced models that convert these data into meaningful information to deal with. Hence, different chemometric models were applied for the analysis of data obtained from a mixture containing sofosbuvir, ledipasvir, velpatasvir, daclatasvir, and valacyclovir that were recently used internationally for their antiviral activity. Methods: Partial Least Squares, Spectral Residual Augmented Classical Least Squares, and Concentration Residual Augmented Classical Least Squares designs were applied with and without variable selection procedure [Genetic Algorithm (GA)]. The methods were used for the quantitative analysis of the drugs in laboratory prepared mixtures and real market sample through handling the UV spectral data. Results: Robust models were obtained by applying GA. The proposed methods were found to be rapid, simple, and required no preliminary separation steps. Conclusion: These models can be used on a routine basis in quality control laboratories or factories giving competitor results to those obtained by the reported methods. Highlights: The proposed models offer a powerful analytical alternative for laboratories that consider economic strategies in their requirements.