The application of an artificial neural network and pharmacokinetic simulations in the design of controlled-release dosage forms.

The objective of this work is to use an artificial neural network (ANN) and pharmacokinetic simulations in the design of controlled-release formulations with predictable in vitro and in vivo behavior. Seven formulation variables and three other tablet variables (moisture, particle size and hardness) for 22 tablet formulations of a model sympathomimetic drug were used as the ANN model input, and in vitro dissolution-time profiles at ten different sampling times were used as output. An ANN model was constructed by selecting the optimal number of iterations and model structure (the number of hidden layers and number of hidden layer nodes). The optimized ANN model was used for prediction of formulations based on two desired target in vitro dissolution-time profiles and two desired bioavailability profiles. For three of the four predicted formulations there was very good agreement between the ANN predicted and the observed in vitro and simulated in vivo properties. This work illustrates the potential for an artificial neural network, along with pharmacokinetic simulations, to assist in the development of complex dosage forms.