A Comparative Chemometric Study for Quantitative Determination of Duloxetine Hydrochloride in the Presence of its Toxic Impurity 1-Naphthol - Anwar, Basma H.; Abdelhamid, Nessreen S.; Magdy, Maimana A.; Naguib, Ibrahim A. (Current Pharmaceutical Analysis, 2020)
Background: Duloxetine hydrochloride (DUL) is a serotonin-norepinephrine reuptake inhibitor. It is used for treating depression and anxiety. It is available in the market as a capsule called Cymbatex®, which is used for the treatment of depression. 1-naphthol is a potential impurity of DUL. It is hepatotoxic to humans and has potential toxicity to freshwater fish. Objective: Duloxetine hydrochloride was determined in the presence of its toxic impurity 1-naphthol in raw material and in pharmaceutical dosage forms using three multivariate calibration chemometric methods. Methods: Classical Least Squares (CLS), Partial Least-Squares (PLS) and linear support vector regression (SVR) were developed using UV spectral data. The three methods were compared among each other and the advantages and disadvantages were discussed. For good results, a two-factor four-level experimental design was used, resulting in a training set of 16 mixtures containing different ratios of each component. The test set consisting of nine mixtures was necessary to test the ability of the proposed methods to predict DUL in the presence of its impurity, 1-naphthol. Results: The results show the success of the three developed methods to determine DUL in the presence of small levels of its toxic impurity with good accuracy and selectivity. The results of the dosage form were compared statistically to that of the reported HPLC method, with no significant difference in accuracy and precision. Conclusion: The suggested calibration models are suitable for routine analysis of the drug in bulk and pharmaceutical dosage forms. Compared to the CLS and PLS models, the SVR model gives the best results regarding the accuracy with a lower prediction error and better generalization ability. However, the CLS and PLS models are found to be simpler and faster in usage and management.
Keywords: classical least squares