Pure component contribution (PCCA) and synergy interval partial least squares (siPLS) algorithms for efficient resolution and quantification of overlapped signals; an application to novel antiviral tablets of daclatasvir, sofosbuvir and ribavirin

Abstract

Daclatasvir (DAC), sofosbuvir (SOF) and ribavirin (RIB) have been recently co-formulated in tablet dosage form for the treatment of Hepatitis C virus infections. In this work, the resolution and quantitation of overlapped spectral signals was achieved by both univariate and multivariate algorithms. Pure component contribution algorithm (PCCA) as a novel approach was applied along with factor based partial least squares (PLS) algorithms using both full range and synergistic intervals (siPLS). Each drug could be determined at its λ_max using PCCA, while PLS and siPLS were used for multivariate determination of the three components. Good linear relationships were obtained in the ranges of 5.45-16.35, 4.40-44.00 and 5.50-35.00 µg/mL for DAC, SOF and RIB, respectively, by PCCA. The PLS and siPLS models were built for the three compounds each in the concentration range of 2.00-10.00, 10.00-20.00 and 10.00-26.00 µg/mLfor DAC, SOF and RIB, respectively. Validation of the proposed methods was ascertained according to ICH guidelines for PCCA and through the use of internal and external validation sets for PLS and SiPLS models. The three methods were successfully applied for determination of DAC, SOF and RIB in pure form and in tablets.