Augmented least squares, a powerful chemometric approach for the spectroscopic analysis of the antiretroviral therapy abacavir, lamivudine and dolutegravir in their ternary mixture.

Augmented least squares models such as concentration residual augmented classical least squares (CRACLS) and spectral residual augmented classical least squares (SRACLS) are powerful chemometric approaches that can be applied for spectroscopic analysis of many pharmaceutical compounds. Herein, both CRACLS and SRACL have been employed for UV spectral analysis of three antiretroviral therapy namely abacavir (ACV), lamivudine (LMV) and dolutegravir (DTG) in their ternary mixture. A partial factorial design has been utilized for calibration set construction then both CRACLS and SRACLS models have been optimized regarding the number of iterations and principal components, respectively, using a leave-one-out cross-validation procedure. It was found that a higher number of iterations and principal components were required for modelling the minor component DTG indicating more augmentation procedures to improve the models' accuracy. Validation of the proposed models was performed using external validation set of 13 mixtures and different validation parameters have been evaluated regarding models' predictive abilities. Both models showed excellent performance for analyzing ACV and LMV with relative root mean square error of prediction (RRMSEP) below 2 %. However, higher RRMSEP values around 5 % were observed for the minor component DTG suggesting that these models should be utilized with caution when analyzing minor components in mixtures. Furthermore, the suggested models have been applied for analyzing ACV, LMV and DTG in their pharmaceutical formulation and excellent agreement was observed between the suggested models and the reported chromatographic method posing these models as powerful chemometric approaches for quality control analysis of many pharmaceutical compounds.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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