TAILIEUCHUNG - An approach of soft-computing in optimizing controlled release products

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. | TẠP CHÍ PHÁT TRIỂN KH&CN, TẬP 13, SỐ T2 - 2010 AN APPROACH OF SOFT-COMPUTING IN OPTIMIZING CONTROLLED RELEASE PRODUCTS Nam Phuong Nguyen, Nam Huu Bui, Duong Quang Do University of Medicine and Pharmacy Ho Chi Minh City ABSTRACT: In the pharmaceutical market, all products have a life cycle Out of date products should be replaced by new ones, which have better quality.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. Keywords: Neural networks, Genetic Algorithms, Optimization, Soft computing, Controlled Release. space to find the point, which has the optimum 1. INTRODUCTION balance of properties. Nowadays, formulators Formulation design is regular work of pharmacist because all products have a life cycle products quality need to be constantly improved. Out of date products should be replaced by better ones. For this reason, modelling and optimization of formulation are the regular demands [1] . Traditional methods of design and optimization of formulation - such as statistics, simplex,. - are only used for simple and linear data. These methods are not suitable for complicated or non-linear data. The formidable task of formulation research is to navigate multidimensional .

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