Application of Neural Computing in Pharmaceutical Product Development: Computer Aided Formulation Design

Abstract

Most pharmaceutical products are complex systems designed to meet several compendial or other performance standards simultaneously. Ideal or ‘optimum’ product composition and the manufacturing process variables are generally established after extensive experimentation. Artificial Neural Networks are pattern recognition tools that allow the development of ‘expert’ systems without having to write computer programs. With this technology it may be possible to develop formulation ‘expert’ systems to predict the formulation composition and the manufacturing process conditions necessary to achieve the desired performance standards. This report introduces the concept of a formulation expert system to predict the in vitro drug release profile from hydrophilic matrix tablets. Formulation expert systems or Computer Aided Formulation Design has the potential to reduce the time and cost of the product development process.