Formula optimization of theophylline controlled-release tablet based on artificial neural networks

Formulation of the controlled-release tablet containing theophylline was optimized based on the simultaneous optimization technique in which an artificial neural network (ANN) was incorporated. As model formulations, 16 kinds of theophylline tablets were prepared. The amounts of Controse, the mixture of hydroxypropylmethyl cellulose with lactose, cornstarch and compression pressure were selected as causal factors. The release profiles of theophylline were characterized as the sum of the fast and slow release fractions. The initial weight, the rate constant in the fast release fraction and the rate constant in the slow release fraction were estimated as release parameters. A set of release parameters and causal factors were used as tutorial data for ANN and analyzed using a computer. Based on the plasma concentration profiles of theophylline predicted by the pharmacokinetic analysis in humans, a desirable set of release parameters was provided. The simultaneous optimization was performed by minimizing the generalized distance between the predicted values of each response and the desirable one that was optimized individually. The optimization technique incorporating ANN showed a fairly good agreement between the observed values of release parameters and the predicted results.