Application of Artificial Neural Networks (ANN) in the Development of Solid Dosage Forms

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Abstract
The application of ANN in pharmaceutical development has been assessed using theoretical as well as typical pharmaceutical technology examples. The aim was to quantitatively describe the achieved data fitting and predicting abilities of the models developed with a view to using ANN in the development of solid dosage forms. The comparison between the ANN and a traditional statistical (i.e., response surface methodology, RSM) modeling technique was carried out using the squared correlation coefficient R2. Using a highly nonlinear arbitrary function the ANN models showed better fitting (R2 = 0.931 vs. R2 = 0.424) as well as predicting (R2 = 0.810 vs. R2 = 0.547) abilities. Experimental data from a tablet compression study were fitted using two types of ANN models (i.e., multilayer perceptrons and a hybrid network composed of a self-organising feature map joined to a multilayer perceptron). The achiedved data fitting was comparable for the three methods (MLP R2 = 0.911, SOFM-MLPR2 = 0.850, and RSM R2 = 0.897). ANN methodology represents a promising modeling technique when applied to pharmaceutical technology data sets.