Multi-objective simultaneous optimization based on artificial neural network in a ketoprofen hydrogel formula containing O-ethylmenthol as a percutaneous absorption enhancer

The aim of this study was to apply a novel simultaneous optimization technique incorporating an artificial neural network (ANN) to a design of a ketoprofen hydrogel containing O-ethylmenthol (MET). For model formulae, 12 kinds of ketoprofen hydrogels were prepared. The amount of ethanol and MET were selected as causal factors. A percutaneous absorption study in vivo in rats was performed and irritation evoked on rat skin was microscopically judged after the end of the experiments. The rate of penetration (R-p), lag time (t(L)) and total irritation score (TIS) were selected as response variables. A set of causal factors and response variables was used as tutorial data for ANN and fed into a computer. Nonlinear relationships between the causal factors and the release parameters were represented well with the response surface predicted by ANN. The optimization of the ketoprofen hydrogel was performed according to the generalized distance function method. The observed results of R-p and TIS, which had a lot of influence on the effectiveness and safety, coincided well with the predictions. It was suggested that the multi-objective simultaneous optimization technique incorporating ANN Was quite useful for optimizing pharmaceutical formulae when pharmaceutical responses were nonlinearly related to the formulae and process variables. (C) 1997 Elsevier Science B.V.