The information generated during pharmaceutical product development is voluminous and diverse [1]. Current applications that manage this information include applications based on relational databases [2] or document management systems [3]. In addition, mathematical models and heuristics about unit operations are frequently embedded in the source code [4], making it difficult to access and update. An efficient information management platform, necessary for the profitability of the enterprise, would require a common data model to consolidate and effectively model the disparate pieces of information from multiple sources in the domain.

Ontology is the explicit representation of concepts and their relationship [5]. Due to the potential of ontology to represent the concepts in a complex domain (pharmaceutical product development), the Purdue Ontology for Pharmaceutical Engineering (POPE) was developed. POPE has components to model phases, molecular structure, reactions, material properties, experiments, unit operations and equipment. Several applications that utilize POPE were developed; including an application for prediction of potential reactions a drug substance may undergo and an application to analyze experiments with respect to their settings and procedures. In this work, the Purdue Ontology for Pharmaceutical Engineering, as well as the applications that make use of it, are discussed.

References

1. Venkatasubramanian V., C. Zhao, G. Joglekar, A. Jain, L. Hailemariam, P. Suresh, V. Akkisetty, K. Morris and G.V. Reklaitis (2006) Ontological Informatics Infrastructure for chemical product design and process development. Computers and Chemical Engineering. CPC 7 Special Issue, 30(10-12) 1482-1496

2. Paszko C. and C. Pugsley (2000) Considerations in Selecting a Laboratory Information Management System (LIMS). American Laboratory. 9 38-42

3. Zall M. (2001) The Nascent Paperless Laboratory. Chemical Innovation. 31 2-9

4. Suresh P., C. Zhao, C. Joglekar and V. Venkatasubramanian. (2006) Informatics Based Approach for Mathematical Knowledge Modeling in Process Operations. AIChE Annual Meeting, November 12-17 San Francisco Hilton, San Francisco, CA

5. Gruber T. (1993) Toward principles for the design of ontologies used for knowledge sharing Internation al Journal of Human Computer Studies 43 907-928