Knowledge-Based Systems and Other AI Applications for Tableting

The pharmaceutical industry is under continual pressure to speed up the drug development process, reduce costs, and improve process design. At the same time, FDA’s new Process Analytical Technology initiatives encourage the building of product quality and the development of meaningful product and process specifications that are ultimately linked to clinical performance. Together, these two issues present significant challenges to formulation and process scientists because of the complex, typically non-linear, relationships that define the impact of multiple formulation and process variables (independent variables) and such outcome responses (dependent variables) as drug release, product stability, and others. The number of variables that must be addressed is substantial and include, for example, the level of drug substance, the types and levels of various excipients, potential drug-excipient interactions, and their potential positive or negative interactions with a host of process variables. Often, the relationships between these variables and responses are not understood well enough to allow precise quantitation. And, since an optimal formulation for one response is not necessarily an optimal formulation for another response, product development is further confounded by the need to optimize a number of responses simultaneously. <p> Clearly, formulation scientists work in a complex, multidimensional design space. In recent decades, scientists have turned more and more to such tools as multivariate analysis and response surface methodology, knowledge-based systems and other artificial intelligence applications to identify critical formulation and process variables, to develop predictive models, and to facilitate problem solving and decision making in product development. The goal of this chapter is to address artificial intelligence applications and describe their role in supporting formulation and process development.