Formulation optimization technique based on artificial neural network in salbutamol sulfate osmotic pump tablets.

The aim of this study was to develop a formulation optimization technique in which an artificial neural network (ANN) was incorporated; 30 kinds of salbutamol sulfate osmotic pump tablets were prepared, and their dissolution tests were performed. The amounts of hydroxypropyl methylcellulose (HPMC), polyethylene glycol 1500 (PEG1500) in the coating solution, and the coat weight were selected as the causal factors. Both the average drug release rate v for the first 8 hr and the correlation coefficient r of the accumulative amount of drug released and time were obtained as release parameters to characterize the release profiles. A set of release parameters and causal factors was used as training data for the ANN, and another set of data was used as test data. Both sets of data were fed into a computer to train the ANN. The training process of the ANN was completed until a satisfactory value of error function E for the test data was obtained. The optimal formulation produced by the technique gave the satisfactory release profile since the observed results coincided well with the predicted results. These findings demonstrate that an ANN is quite useful in the optimization of pharmaceutical formulations.