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 incorpora ting an artificial neural network (ANN) was applied to a design of a ketopr ofen 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 w as 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 impr oved by the addition of a small amount of MET, whereas the diffusivity of t he drug was enhanced at higher concentration of MET (0.5-1%). For the optim ization study, the amount of ethanol and MET were selected as causal factor s. A rate of penetration (R-p) and lag time (t(L)) and total irritation sco re (TIS) were selected as response variables. A set of causal factors and r esponse variables was used as tutorial data for ANN and fed into a computer . Nonlinear relationships between the causal factors and the response varia bles were represented well with the response surface predicted by ANN. The optimization of the ketoprofen hydrogel was performed according to the gene ralized distance function method. The observed results of R-p and TIS, whic h had a lot of influence on the effectiveness and safety, coincided well th e predictions. (C) 1999 Elsevier Science B.V. All rights reserved.