From Black-Box to Transparent Computational Intelligence Models: A Pharmaceutical Case Study

Abstract

Pharmaceutical Industry is tightly regulated owing to health concerns. Over the years, the use of computational intelligence (CI) tools has increased in the pharmaceutical research and development, manufacturing, and quality control. CI models for tensile strength of tablets based on the formulation design and manufacturing parameters have been established. Best models exhibit NRMSE of 7.2%. Implicitly, CI tools work in a black -- box fashion which makes it difficult to inspect the model. Owing to quality and safety concerns, it is imperative for the pharmaceutical industry and the regulatory authorities to have knowledge of how the CI models work. This work uses data from a galencial tableting study to establish models for the outcome of tensile strength from various CI techniques and makes an attempt to make the models as transparent as possible. Tree based ensembles and symbolic regression methods are presented as transparent models with extracted rules and mathematical formula, respectively, explaining the CI models in greater detail.