Formula optimization based on artificial neural networks in transdermal drug delivery

The promoting effect of O-ethylmenthol (MET) on the percutaneous absorption of ketoprofen from alcoholic hydrogels was evaluated in rats in vitro and in vivo. Furthermore, a novel simultaneous optimization technique incorporating an artificial neural network (ANN) was applied to a design of a ketoprofen hydrogel containing MET. When a small quantity of MET (0.25-0.5%) was added to the hydrogels, the permeation of ketoprofen increased remarkably, compared with the control. On the other hand, little change in permeation was observed when small amounts of menthol were used (<1%), and at least 2% menthol was required to obtain a promoting efficiency comparable with 0.25% MET. The partitioning of ketoprofen from the hydrogel to the skin was improved by the addition of a small amount of MET, whereas the diffusivity of the drug was enhanced at higher concentration of MET (0.5-1%). For the optimization study, the amount of ethanol and MET were selected as causal factors. A rate of penetration (R(p)) and 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 response variables 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 the predictions.