Purdue Ontology for Pharmaceutical Engineering: Part I. Conceptual Framework

Abstract

Introduction In pharmaceutical drug development and manufacturing, the amount and complexity of information of different types, ranging from raw experimental data to lab reports to complex mathematical models that needs to be stored, accessed, validated, manipulated, managed, and used for decision making is staggering. The information is often in different formats, used in different computer tools, making smooth interaction between these tools difficult. A common, explicit, and platform-independent vocabulary that is both machine accessible and human usable is needed to streamline the flow of information and knowledge generation. Methods The Purdue Ontology for Pharmaceutical Engineering (POPE) was developed to address this informatics challenge. POPE models information and knowledge and includes models of phases, material properties, molecular structures, experiments, reactions, and unit operations. Conclusion In Part 1, we describe the conceptual framework of POPE and in Part 2 its applications.

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.…”

Role of Knowledge Management in Development and Lifecycle Management of Biopharmaceuticals

“…Because of this, a common, explicit, and platform-independent vocabulary that is both machine accessible and human usable is needed to streamline the generation and flow of knowledge (18). An ontology defines and semantically describes data and information.…”

“…Therefore, several authors have extensively studied them in different areas: risk management (73,74), product formulation, unit operation model integration (18), determination of CQAs and CPPs (75), mathematical model storage, use, and solving (19), regulatory compliance (76), manufacturing execution systems (77) and drug discovery (78). …”

“… Maps, Taxonomies and ontologies While taxonomies and maps are hierarchical representations of knowledge, ontology defines and semantically describes the data and information, being the basis for modeling different forms of knowledge. They create a common, explicit, and platform-independent vocabulary that is both machine accessible and human usable.Holistic ontologies in pharmaceutical engineering (18), ontologies for pharmaceutical mathematical knowledge modeling (19)Analysis Visualization Information arrangement that displays a set of data easily understood by a wide audience. Graphs, charts and Response Surface Methods are included.Visualizing metabolic networks (20), Response Surface Methodology in biotechnology (21) Statistical analyzers Provide quantitative measure of the inherent variability of a phenomena, assessing the current state of control and enabling process improvement.Chemometrics-based PAT (22), Integrated statistical platforms to laboratory management (23) Intelligent agents Decision support tools that are grounded in the emulation of skills for reasoning and inference.…”

Purdue Ontology for Pharmaceutical Engineering: Part II. Applications

“…These include the Purdue Ontology for Material Entities (POME) which describes phases, substances, and compositions; Purdue Ontology for Molecular Structure (POMS) which describes molecular structures; Purdue Ontology for Reaction Engineering (PORE) which describes chemical and physical reactions; Purdue Ontology for Material Properties (POMP) which describes physical and chemical properties; Purdue Ontology for Description of Experiments (PODE) which describes experiments, procedures, and settings; Purdue Ontology for Characterization of Equipment, Purdue Ontology for Description of Unit Processes and the Purdue Ontology for Value Description. Further description of these ontologies is given in Part I of this paper [4]. Once developed, these ontologies can be populated with information and data from relational databases or text repositories (constituting the knowledge base) using specialized tools as described in this work.…”