Comparison of Four Artificial Neural Network Software Programs Used to Predict the In Vitro Dissolution of Controlled-Release Tablets

Abstract

The purpose of this study was to evaluate four commercially available artificial neural network (ANN) software programs: NeuroShell2 v3.0, BrainMaker v3.7, CAD/Chem v5.0, and NeuralWorks Professional II/Plus for prediction of in vitro dissolution-time profiles of controlled-release tablets containing a model sympathomimetic drug. Seven independent formulation variables and three other tablet variables (moisture content of granules, granule particle size, and tablet hardness), for 22 tablet formulations, were used as the ANN model input. In vitro dissolution time-profiles at 10 different sampling times were used as the output. The models' optimum architectures were determined for each ANN software by varying the number of hidden layers and number of nodes in hidden layer(s). The ANN developed from the four software programs were validated by predicting the in vitro dissolution time-profiles of each of the 19 formulations, which were excluded from the training process. Although the same data set was used, the optimum ANN architectures generated from the four software programs were different. Using the four optimum ANN models, the plots of predicted vs. observed percentage of drug dissolved gave slopes ranging from 0.95 to 1.01 and r2 values ranging from 0.95 to 0.99 for all 190 dissolution data points for the 19 training formulations. The difference factors (f1) and similarity factors (f2) between the ANN predicted and the observed in vitro dissolution profiles were also used to compare the predictions for the four software programs. It was concluded that the four programs provided reasonable predictions of in vitro dissolution profiles for the data set employed in this study, with NeuralShell2 showing the best overall prediction.