Purdue Ontology for Pharmaceutical Engineering: Part II. Applications

Abstract

The multiple steps in pharmaceutical product development generate a large amount of diverse information in various formats, which hinders efficient decisionmaking. A major component of the solution is a common information model for the domain. Ontologies were found to meet this need as described in Part I of this two-part paper. In Part II, we describe two applications of Purdue Ontology for Pharmaceutical Engineering. The first application deals with the prediction of degradation reactions through incorporation of molecular structure and environmental information captured in the ontologies. The second application is one that analyzes experiments to identify differences in experimental implementation.

Systems Approach to Process Lifecycle Engineering in Pharmaceutical Production

“…The ontology-based knowledge integration framework has been studied and is used for modeling guideline knowledge and mathematical knowledge generated through the research and development process . Purdue University introduced a method of constructing the ontology for pharmaceuticals engineering and application (Hailemariam, 2010a;Hailemariam, 2010b). If ontology is designed without activity models, it may not be free from excess and deficiency.…”

“…This information warehousing system with related activity model supports information retrieval and utilization in response to the demand by each activity. Some published methods are available for designing detailed systems to warehouse and search such information (Hailemariam, 2010a(Hailemariam, , 2010b.…”

From conceptual design to process design optimization: a review on flowsheet synthesis

“…In the domain of chemical and process engineering, the number of available ontologies is increasing and demonstrating the growing interest of the community. Among them, one can mention the following to facilitate the identification of the best option for the optimization problem: process supervision of large scale chemical plants (Natarajan et al, 2012); computer-aided process engineering (Morbach et al, 2007(Morbach et al, , 2009); batch process (Muñoz et al, 2010); process integration and interoperability (Muñoz et al, 2013); process simulation and optimization in the context of eco-industrial parks (Zhou et al, 2017); pharmaceutical product development and manufacturing (Hailemariam and Venkatasubramanian, 2010;Venkatasubramanian et al, 2006); petrochemical processes (Ni et al, 2011), and biomass and biorefineries (Trokanas et al, 2016).…”

The Future of Pharmaceutical Manufacturing Sciences

“…It is the responsibility of academia, industry, and regulators to provide them, which requires concepts for resolving the lack of data management and providing opportunities of prospective and retrospective consideration. One possible approach with that regard are ontologies or other knowledge management tools. Another future challenge in manufacturing science is improving communication within the QRM framework.…”