Principal Component Artificial Neural Network Calibration Models for Simultaneous Spectrophotometric Estimation of Phenobarbitone and Phenytoin Sodium in Tablets

Satyanarayana Dondeti,* Kamarajan Kannan, and Rajappan Manavalan
Department of Pharmacy, Annamalai University, Annamalainagar, Tamil Nadu-608002, India,
E-mail: sand60@rediffmail.com

Abstract
Simultaneous estimation of all drug components in a multicomponent pharmaceutical dosage form with artificial neural networks calibration models using UV spectrophotometry has been reported as a simple alternative to using separate models for each component. A novel approach for calibration using computed spectral dataset derived from three spectra of each component has been described. Spectra of Phenobarbitone and Phenytoin sodium were recorded at several concentrations within their linear range and used to compute the calibration mixture between wavelengths 220 to 260 nm at an interval of 1 nm. Principal component back-propagation neural networks trained by Levenberg-Marquardt algorithm were used for building and optimizing calibration models using MATLAB^® Neural Network Toolbox. Neural network models were compared to principal component regression model. The calibration model was thoroughly evaluated at several concentration levels using spectra obtained for 95 synthetic binary mixtures prepared using orthogonal designs. The optimized model showed sufficient robustness even when the calibration sets were constructed from different set of pure spectra of components. Although the components showed significant spectral overlap, the model could accurately estimate the drugs, with satisfactory precision and accuracy, in tablet dosage with no interference from excipients as indicated by the recovery study results.

Key words: Principal components, artificial neural networks, UV spectrophotometry, Phenobarbitone, Phenytoin sodium.