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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 r(2) (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.

Ziprasidone is known as a novel "atypical" or "second-generation" antipsychotic drug. A sensitive and reproducible method was developed and validated for determination of ziprasidone and its major impurities, which are significantly different in polarity. The separation is performed on a Waters Spherisorb (R) octadecylsilyl 1 column (5.0 mu m particle size, 250 x 4.6 mm id) using a gradient with mobile phase A [buffer acetonitrile (80+20, v/v)] and mobile phase B [buffer acetonitrile (10+90, v/v)] at a working temperature of 25 degrees C. The buffer was 0.05 M KH(2)PO(4) solution with an addition of 10 mL triethylamine/L solution, adjusted to pH 2.5 with orthophosphoric acid. The flow rate was 1.5 mL/min, and the eluate was monitored at 250 nm using a diode array detector. Optimization of the experimental conditions was performed using partial least squares regression, for which four factors were selected for optimization: buffer concentration, buffer pH, triethylamine concentration, and temperature. The proposed validated method is convenient and reliable for the assay and purity control in both raw materials and dosage forms.

An RP-HPLC method for simultaneous separation and quantification of pantoprazole and its five main impurities in pharmaceutical formulations was developed and validated. The separation was accomplished on a Zorbax Eclipse XDB C18 column (5 mu m particle size, 150 x 4.6 mm id) using a gradient with mobile phase A [buffer-acetonitrile (70 + 30, v/v)], and mobile phase B [buffer-acetonitrile (30 + 70, v/v)]. The buffer was 0.01 M ammonium acetate solution with addition of 1 mL triethylamine/L of the solution, adjusted to pH 4.5 with orthophosphoric acid. The eluent flow rate was 1 mL/min, the temperature of the column was 30 C, and the eluate was monitored at 290 nm. Linearity (r = 0.999), recovery (97.6-105.8%), RSD (0.55-1.90%), and LOQ (0.099-1.48 mu g/mL) were evaluated and found to be satisfactory. The proposed method can be used for simultaneous identification and quantification of the analyzed compounds in pharmaceutical formulations.