Abstract

In the pharmaceutical market, all products have a life cycle Out of date products should be replaced by new ones, which have better quality. For this reason, modelling and optimizing formulation are the regular demands. Traditional methods of design and optimization - such as statistics, simplex – can only be used for simple and linear data. In case of complicated or non-linear data, alternative methods that are able to deal with such data are needed. This paper presents a solution for optimizing controlled release product formulation using a combination of AI techniques (Soft-Computing): neural networks, fuzzy logic and genetic algorithms. This achievement will help to significantly reduce time and labour in R&D process thank to its good accuracy and high processing speed. The results obtained from this research indicate that the alternative approach can be considered as an effective and efficient method for modelling and optimising controlled release formulations.