Method Development and Method Validation of Tinidazole in Bulk and Pharmaceutical Dosage Form by UV– Spectrophotometric Method
B. Siddartha, I. Sudheer Babu, Parthiban Chokkalingam, V. Prathyusha
Abstract
B. Siddartha, I. Sudheer Babu, Parthiban Chokkalingam, V. Prathyusha
Abstract
A simple, precise and accurate UV Spectrophotometric method has been developed for estimation of Tinidazole in bulk and tablet dosage form. In this method Tinidazole shows λmax at 365nm using 0.1N NaOH as a solvent. The responses were linear in the range of 5–25μg/ml. The regression equation of the calibration graph and correlation coefficient were found to be y = 0.039x + 0.008 and 0.999 respectively. The recovery of the drug from the sample was ranged between 99.09% and 99.23%. The proposed method was validated as per ICH Q2 (R1) guidelines for precision, linearity, accuracy and recovery. The %RSD values for both intraday and interday precision were less than 1%. The limit of detection (LOD) and limit of quantification (LOQ) were found to be 0.034μg/ml and 0.103μg/ml respectively by simple UV spectroscopy.
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A simple, precise and accurate UV Spectrophotometric method has been developed for estimation of Tinidazole in bulk and tablet dosage form. In this method Tinidazole shows λmax at 365nm using 0.1N NaOH as a solvent. The responses were linear in the range of 5–25μg/ml. The regression equation of the calibration graph and correlation coefficient were found to be y = 0.039x + 0.008 and 0.999 respectively. The recovery of the drug from the sample was ranged between 99.09% and 99.23%. The proposed method was validated as per ICH Q2 (R1) guidelines for precision, linearity, accuracy and recovery. The %RSD values for both intraday and interday precision were less than 1%. The limit of detection (LOD) and limit of quantification (LOQ) were found to be 0.034μg/ml and 0.103μg/ml respectively by simple UV spectroscopy.
Key concepts: Tinidazole, Correlation coefficient, Dosage form, Detection limit, Chromatography, Linearity, Mathematics, Linear regression