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In this study, the RP-HPLC method was investigated for the separation of citalopram and its four impurities by use of statistical experimental design. Initially, the influence of different experimental conditions (buffer pH, flow rate, and column temperature) on the chromatographic behavior of citalopram and its four impurities was investigated by use of partial least squares regression (PLSR) and multilayer perceptron (MLP) artificial neural networks (ANNs) trained by back-propagation. The developed models and the corresponding response surface plots were used to select the optimal HPLC conditions, buffer pH 7.0, flow rate 1.0 mL/min, and column temperature 25°C, for an efficient separation of citalopram and its four impurities. The elaborated HPLC method was found to be linear, specific, sensitive, precise, accurate, and robust. Retention times of citalopram and its impurities, obtained with the developed HPLC method, and the computed molecular parameters of the examined compounds were used in a quantitative structure retention relationship (QSRR) study. The PLSR and ANN algorithms were applied for the development of the QSRR methods. The MLP-two layers-ANN-QSRR model with root mean square error of prediction 0.105 and r2 (observed versus predicted) 0.978 was selected. Since many different reaction conditions are applied for the synthesis of citalopram, different impurities and degradation products can be formed. Therefore, the developed QSRR model can be extended to the prediction of the retention times with the other citalopram impurities, degradation products, and metabolites.

Development and Optimization of an HPLC Analysis of Citalopram and Its Four Nonchiral Impurities Using Experimental Design Methodology