| Research Abstract |
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 RSM, is often limited to low levels, resulting in poor estimations of optimal formulations. A multi-objective simultaneous optimization method incorporating an artificial neural network (ANN) was developed. The method was applied to the optimization of ketoprofen hydrogel formulations including 1-O-ethyl-3-n-butylcyclohexanol as absorption enhancer. ANNs are being increasingly used in pharmaceutical research to predict the nonlinear relationship between causal factors and response variables. The observed results of several characteristics in the optimum formulations coincided well with the predictions, suggesting superior function of the ANN approach.
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