(677d) An Ontology Based Approach for Knowledge Modeling in Pharmaceutical Product Development | AIChE

(677d) An Ontology Based Approach for Knowledge Modeling in Pharmaceutical Product Development


Jain, A. - Presenter, School of Chemical Engineering, Purdue University
Zhao, C. - Presenter, Bayer Technology and Engineering (Shanghai) Co. Ltd
Hailemariam, L. - Presenter, Purdue University
Akkisetty, V. P. K. - Presenter, Purdue University
Suresh, P. - Presenter, Pudue University
Morris, K. R. - Presenter, Purdue University
Reklaitis, G. V. - Presenter, Purdue University

Development of a drug product is a complex, iterative process consisting of selection of a dosage form, excipients, processing route, operating equipment and so on. At each stage, knowledge in various forms, including heuristics, decision trees and mathematical models, is used in making decisions. Typically knowledge is modeled specifically for the tool that uses it, such as expert systems and simulation tools. This makes it very difficult to share the knowledge across different tools and among development teams, and integrate various forms of knowledge to assist in making pharmaceutical product development decisions. To provide easier access to available knowledge and better decision support, we propose an integrated approach to systematically model the different forms of knowledge.

An ontology based approach is used to model the knowledge in form of guidelines, which are created based on GLIF (GuideLine Interchange Format), a specification developed mainly for structured representation of clinical guidelines (Peleg et al., 2004). A guideline models procedural knowledge, which consists of decision logic, information look up, evaluation of decision variables and providing the recommendations. For example, to determine whether direct compaction is appropriate for a particular drug substance, values of several properties, such as flowability, compressibility are examined.

Guidelines are developed based on the knowledge captured from detailed discussions with the faculty in Industrial Pharmacy at Purdue University and experts from various pharmaceutical companies. Guidelines are computer interpretable as well as human readable and they interact with other forms of knowledge including mathematical models which are modeled in form of ontologies. A simple but complete representation model for guidelines makes it easier for the domain experts to interact directly with the guidelines and add/modify them based on the requirements. In order to provide the decision support, a java based execution engine is developed for implementation of a guideline. The engine is linked to the guideline and other ontologies directly and use the knowledge in the guideline and the information stored in an ontology-based information repository to provide decision support. As the guidelines are executed and decisions are made, the details of the state of the product, such as selected excipients, are represented as development state.

Reformulation of a MDRTB (multi-drug resistance tuberculosis) drug is used as a case study to demonstrate the applicability and benefits of the proposed approach. The guidelines are used for recommending processing route and route dependent excipients to manufacture the drug product as immediate release oral solid dosage form. To select excipients these guidelines use mixing rules to predict the mixture properties such as flow properties. The mixing rule that computes the mixture properties as the weighted average of pure component properties are used. Mixing rules and other mathematical knowledge are modeled in form of ontologies and accessed directly by the guideline execution engine.

This approach provides an open and easy way to create, use and modify the knowledge and supports the integration of the various forms of knowledge and information. It could also be used to effectively capture the knowledge in different domains including process development and operations in chemical and pharmaceutical industry.

Keywords: Pharmaceutical Product Development, Formulation, Knowledge Modeling, Guidelines, Ontologies

References: Peleg, M., Boxwala, A., Tu, S., Wang, D., Ogunyemi, O., Zeng, Q., 2004. Guideline Interchange Format 3.5 Technical Specification, http://www.glif.org/