Different software programs based on mathematical models have been developed to aid the\r\nproduct development process. Recent developments in mathematics and computer science\r\nhave resulted in new programs based on artificial neural networks (ANN) techniques. These\r\nprograms have been used to develop and formulate pharmaceutical products. In this study,\r\nintelligent software was used to predict the relationship between the materials that were used\r\nin tablet formulation and the tablet specifications and to determine highly detailed information\r\nabout the interactions between the formulation parameters and the specifications. The input\r\ndata were generated from historical data and the results obtained from analyzing tablets\r\nproduced by different formulations. The relative significance of inputs on various outputs\r\nsuch as assay, dissolution in 30 min and crushing strengths, was investigated using the\r\nartificial neural networks (ANNs), neurofuzzy logic and genetic programming (FormRules,\r\nINForm ANN and GEP).\r\nThis study indicated that ANN and GEP can be used effectively for optimizing formulations\r\nand that GEP can be evaluated statistically because of the openness of its equations.\r\nAdditionally, FormRules was very helpful for teasing out the relationships between the inputs\r\n(formulation variables) and the outputs.
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