Abstract

Cyclizine (CYZ), a commonly used antiemetic drug, has two pharmacopeial toxic impurities, 1-methylpiperazine (MPZ) and diphenylmethanol (DPM). When CYZ parenteral formulations are administered intravenously, both impurities are poisonous, toxic, and harmful to the human body. Cyclizine was determined along with its hazardous impurities MPZ and DPM by green multivariate calibration using UV-spectroscopic data. Three multivariate algorithms were used to resolve and quantify overlapped spectral signals: principal component regression (PCR), partial least squares (PLS), and synergistic intervals partial least squares (siPLS). A concentration set containing 16 distinct combinations of CYZ, MPZ, and DPM was randomly prepared, and the absorbance values of the concentration set were determined using the 376 point-wavelength set with an interval of 0.2 nm between 200 and 275 nm. Good linear correlations were established for CYZ, MPZ, and DPM in the concentration ranges of 5.00-25.0, 0.50-2.50, and 0.50-2.50 µg/mL, respectively. The ideal spectral range and associated combinations were chosen based on the lowest root mean error of prediction (RMSEP) and correlation coefficient values (r). The siPLS approach performed better than the PCR and PLS models. The combination of four subintervals, 1, 3, 4, and 7, demonstrated the greatest effect, with RMSEP values of 0.0272, 0.0053, and 0.0315 for CYZ, MPZ, and DPM, respectively, and correlation coefficients of 0.9991, 0.9999, and 0.9997, in order. Various assessment tools were used to evaluate and measure the greenness profile of the established methods. The proposed methods were validated using internal and external validation sets. The three methods were effectively used to determine CYZ in its pure form and parenteral formulations, as well as its toxic impurities. The acquired results were compared statistically to those obtained using the reported HPLC method. Cyclizine and its toxic impurities can be determined spectrophotometrically by using the three developed chemometric models.

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