OntoMODEL: Ontological Mathematical Modeling Knowledge Management in Pharmaceutical Product Development, 1: Conceptual Framework

Pharmaceutical product development is a critical step in the path of a drug therapy from its discovery to its delivery to the patient. It is capital-intensive, time-consuming, and extremely information- and knowledge-intensive. This presents various challenges to manage the information and knowledge involved in a systematic, reusable, and user-friendly manner. Knowledge, in this context, means decision-making knowledge and mathematical knowledge that capture the families of mathematical models that exist in this domain. In this paper, which is the first of this two-part series of papers, we describe OntoMODEL, which is an ontological tool for mechanistic mathematical model management that facilitates systematic and standardizable methods for model storage, usage, and solution. [Suresh and co-workers have presented discussions on OntoMODEL at AIChE meetings in San Francisco, CA (2006) and Salt Lake City, UT (2007), as well as at the 18th European Symposium on Computer-Aided Process Engineering (ESCAPE-18) in Lyon, France (2008).] While the declarative knowledge in mathematical models is captured using ontologies, the procedural knowledge required for solving these models is handled by commercially available scientific computing software such as Mathematica and an execution engine written in Java. The interactions involved are well-established and the approach-intuitive; therefore, they do not require user familiarity with any particular programming language or modeling software. Apart from this key benefit, the fact that OntoMODEL lends itself to more-advanced applications such as model-based fault diagnosis, model predictive control (which is decribed in the second paper of this series), knowledge-based decisionmaking, and process flowsheet simulation, making it a useful tool in the intelligent automation of process operations. This paper describes the framework and use of OntoMODEL and discusses how it overcomes the shortcomings of existing approaches toward managing mathematical modeling knowledge.