Formulation optimization of photocrosslinked polyacrylic acid modified with 2-hydroxyethyl methacrylate hydrogel as an adhesive for a dermatological patch.

A photopolymerization technique was applied in the preparation of a hydrogel composed of polyacrylic acid (PAA) in which 2-hydroxyethyl methacrylate (HEMA) was modified. The formulation of photocrosslinked PAA modified with HEMA hydrogel as an adhesive for a dermatological patch was optimized based on the simultaneous optimization technique. Photocrosslinked PAA modified with HEMA hydrogels that retained a large amount of water, above 85%, were successfully prepared. Based on the analysis of ANOVA, the gel strength and adhesiveness increased with an increase in the degree of modification with HEMA and the concentration of PAA modified with HEMA in the aqueous solution. For the optimization study, the modification with HEMA and the concentration of initiator were selected as causal factors. Gel yield, probe tack, degree of swelling and turbidity were selected as response variables. A set of causal factors and response variables was used as a tutorial date for the prediction of optimal formulation with a quadratic regression model, an artificial neural network (ANN) and a multivariate spline interpolation (MSI). Response surfaces generated with MSI well represented the nonlinear relationship between the factors and the responses, and all the observed values of the response variables coincided with the predictions. A high functional photocrosslinked PAA modified with HEMA hydrogel as an adhesive for a dermatological patch was successfully created using the simultaneous optimization technique incorporating MSI.

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.