Neural network based optimization of drug formulations

A pharmaceutical formulation is composed of several formulation factors and process variables. Several responses relating to the effectiveness, usefulness, stability, as well as safety must be optimized simultaneously. Consequently, expertise and experience are required to design acceptable pharmaceutical formulations. A response surface method (RSM) has widely been used for selecting acceptable pharmaceutical formulations. However, prediction of pharmaceutical responses based on the second-order polynomial equation commonly used in an RSM, is often limited to low levels, resulting in poor estimations of optimal formulations. The purpose of this review is to describe the basic concept of the multi-objective simultaneous optimization technique, in which an artificial neural network (ANN) is incorporated. ANNs are being increasingly used in pharmaceutical research to predict the nonlinear relationship between causal factors and response variables. Superior function of the ANN approach was demonstrated by the optimization for typical numerical examples.