Resolution of overlapping spectra using chemometric manipulations of UV-spectrophotometric data for the determination of Atenolol, Losartan, and Hydrochlorothiazide in pharmaceutical dosage form

Abstract

Simultaneous determination of atenolol (ATN), losartan potassium (LOS), and hydrochlorothiazide (HCZ) in presence of HCZ impurity B was conducted by chemometric approaches and radial basis function network (RBFN) using UV-spectrophotometry without preliminary separation. Three chemometric models namely, classical least-squares (CLS), principal component regression (PCR), and partial least-squares (PLS) along with RBFN were utilized using the ternary mixtures of the three drugs. The multivariate calibrations were obtained by measuring the zero-order absorbance of the mixtures from 250 to 270 nm at the interval of 0.2 nm. The models were built covering the concentration range of (4.0 to 20.0), (3.8 to 20.2), and (0.9 to 50.1) μg mL^-1 for ATN, LOS, and HCZ, respectively. The regression coefficient was calculated between the actual and predicted concentrations of the 3 drugs using CLS, PCR, PLS and RBFN. The accuracy of the developed models was evaluated using the root mean square error of prediction (RMSEP) giving satisfactory results. The proposed methods were simple, accurate, precise and were applied efficiently for the quantitation of the three components in laboratory-prepared mixtures, and in dosage form showing good recovery values. In addition, the obtained results were compared statistically with each other using ANOVA test showing non-significant difference between them.

Keywords: Artificial neural networks; Atenolol; Chemometrics; Hydrochlorothiazide; Hypertension; Losartan.