Toward intelligent decision support for pharmaceutical product development

Zhao, Chunhua; Jain, Ankur; Hailemariam, Leaelaf; Suresh, Pradeep; Akkisetty, Pavankumar; Joglekar, Girish; Venkatasubramanian, Venkat; Reklaitis, Gintaras V.; Morris, Ken; Basu, Prabir K.

doi:10.1007/bf02784878

Cited by 56 publications

(23 citation statements)

References 10 publications

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“…The POPE ontology also describes mathematical knowledge, which consists of the mathematical equations as well as the underlying assumptions on the phenomenon. This separates the declarative and procedural components of mathematical models creation, manipulation and solution [5]. The declarative part consisted of two main ontologies, one which represents the details of a model (model definition) such as the model equations and state variables, and the other which represents the details of its use in modeling a specific processing step (model use).…”

Section: The Purdue Ontology For Pharmaceutical Engineeringmentioning

confidence: 99%

“…1), such as expert systems [1][2][3][4][5][6] laboratory information management systems (LIMS) [7,8], electronic lab notebooks [9], content management systems (CMS) [10], etc. They all have tried to address different slices of the overall problem-data, information, and knowledge management issues were addressed separately leading to stand-alone systems with limited capabilities and integration challenges.…”

Section: Introductionmentioning

confidence: 99%

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Purdue Ontology for Pharmaceutical Engineering: Part I. Conceptual Framework

Hailemariam

Venkatasubramanian

2010

J Pharm Innov

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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.

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Section: The Purdue Ontology For Pharmaceutical Engineeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Purdue Ontology for Pharmaceutical Engineering: Part I. Conceptual Framework

Hailemariam

Venkatasubramanian

2010

J Pharm Innov

Self Cite

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“…An ontology is a formal representation of the underlying concepts within a domain. A set of prototype ontologies (Purdue Ontologies for Pharmaceutical Engineering (POPE)) has been developed for the domain of pharmaceutical product formulation [20,21]. The ontologies include components for formally modeling decision processes, material properties, unit operations, and mathematical models.…”

Section: Pharmahub Resourcesmentioning

confidence: 99%

pharmaHUB: Building a Virtual Organization for Pharmaceutical Engineering and Science

2009

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Information technology and high-performance computing play a critical role in the development of virtual organizations for science and engineering disciplines. In this paper, we report on our efforts to design and develop pharmaHUB.org, a prototype virtual organization that intends to promote the joint creation and sharing of tools, knowledge, and educational materials across the various science and engineering disciplines related to pharmaceutical product development and manufacturing. pharmaHUB has been brought online using middleware from HUBzero which provides content management and community building capabilities and also makes it possible to deliver simulation tools through the web. By leveraging the HUBzero middleware and forming partnerships with existing communities of researchers, it has been possible to rapidly develop a prototype virtual organization with a significant offering of educational and simulation tool resources. Prototype tools to support the knowledge management needs of pharmaceutical development have also been integrated into the hub environment.

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“…Further details on the guideline ontologies and mathematical knowledge ontologies are provided elsewhere [2,3]. The information ontologies shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Purdue Ontology for Pharmaceutical Engineering: Part II. Applications

Hailemariam

Venkatasubramanian

2010

J Pharm Innov

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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.

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Toward intelligent decision support for pharmaceutical product development

Cited by 56 publications

References 10 publications

Purdue Ontology for Pharmaceutical Engineering: Part I. Conceptual Framework

Purdue Ontology for Pharmaceutical Engineering: Part I. Conceptual Framework

pharmaHUB: Building a Virtual Organization for Pharmaceutical Engineering and Science

Purdue Ontology for Pharmaceutical Engineering: Part II. Applications

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