Novel approaches to neural and evolutionary computing in pharmaceutical formulation: challenges and new possibilities

The development of commercial pharmaceutical formulations can involve extensive experimentation that generates a large amount of data. Understanding such data and discovering the key relationships within them can be a complex process, adding considerably to the time and expense taken to get a product to market. However, new computational techniques such as neural and evolutionary computing have the potential to accelerate the mining and modeling of data, and these methodologies are now being packaged in a way that makes them readily accessible to the product formulator. This article outlines the basis of these technologies and reviews their use in pharmaceutical formulation, showing that they have gained acceptance as practical research tools, especially when integrated with complementary optimization and visualization tools. Neural and evolutionary computing are gaining widespread acceptance in the field of pharmaceutical formulation, with results being comparable with, or better than, those from traditional statistical methods.