Gani, Rafiqul

Hall, Cierra

Linninger, Andreas

Sin, Gürkan

Online Research Database In Technology

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Downloaded from orbit.dtu.dk on: Dec 19, 2017Systematic Engineering Approach to Development and Identification ofPhysiologically-Based Pharmacokinetic ModelsHall, Cierra; Sin, Gürkan; Gani, Rafiqul; Linninger, AndreasPublication date:2011Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Hall, C., Sin, G., Gani, R., & Linninger, A. (2011). Systematic Engineering Approach to Development andIdentification of Physiologically-Based Pharmacokinetic Models. Abstract from 2011 AIChE Annual Meeting,Minneapolis, MN, United States.Abstract: Systematic Engineering Approach to Development and Identification of Physiologically-Based Pharmacokinetic Models (2011 Annual Meeting)http://aiche.confex.com/aiche/2011/webprogram/Paper236436.html[28-10-2011 15:41:55]Start | Grid View | Browse by Day OR Group/Topical | Author Index | Keyword Index | Personal SchedulerSystematic Engineering Approach to Development and Identification ofPhysiologically-Based Pharmacokinetic ModelsWednesday, October 19, 2011: 4:27 PM101 H (Minneapolis Convention Center)Cierra Hall1, Martina Heitzig2, Gürkan Sin2, Rafiqul Gani2 and Andreas Linninger1, (1)Laboratory for Product and ProcessDesign, University of Illinois at Chicago, Chicago, IL, (2)Department of Chemical and Biochemical Engineering, TechnicalUniversity of Denmark, Kgs. Lyngby, DenmarkWhole body physiologically-based pharmacokinetic (PBPK) modeling attempts to model the uptake and distribution of a druginside the body. Traditionally, pharmacokinetic models have been developed by simple curve fitting of experimental data withoutconsidering the mechanistic and physics of the system. The drawback of such models is their lack of physical insights and thepure extrapolation features to untested conditions, to other drugs as well as to individuals of the same species or different species.Lately, PBPK models, which are based on first principles, have received increasing attention. In this work, a first principlesengineering approach to PBPK modeling that is based on conservation equations and advanced scaling laws is proposed. Thedeveloped global pharmacokinetic models are based on biochemical, anatomical and physiological data.The resulting PBPK models are able to extrapolate and scale between species and individuals because they are based on actualoccurring physical mechanisms and allow the application of more advanced scaling laws than just considering the differing massesof the individuals. Due to their improved scaling qualities, first principles PBPK models have the potential to reduce the number ofundesirable as well as time- and cost-intensive animal experiments during the drug development phase. Further, the developmentof first principle-based PBPK models leads to a gain of insight in the occurring processes during drug uptake by an organism.Having these models at hand moreover allows the determination of accurate and optimal drug dosage for desired therapeuticeffects.However, the development of first principles PBPK models, their identification, and discrimination between different candidatemodels is a non-trivial task inherent with a number of challenges which are related to identifying the occurring mechanisms insidethe body and the appropriate degrees of detail of the models with respect to the modeling goal and the available experimentaldata:Firstly, a large number of alternative PBPK models can be generated depending on the degree of detail considered and theassumptions made about the different considered mechanisms in the body. Further, first principle PBPK models are usuallycomplex models which contain a large number of unknown parameters that are not always all identifiable by the limitedexperimental data accessible. A strategy needs to be found to address the problem of discriminating between the differentcandidate models and identifying their parameters in order to find the model that is best supported by the experimental dataavailable. In this context, design of experiments for model discrimination and parameter estimation plays an important role. If themodels cannot be discriminated based on the available data, or model parameters that are important for the desired model outputsare not identifiable by the available data, methods for optimal design of experiments need to be applied. Design of experiments isespecially important in pharmacokinetic modeling because in order to obtain the required datapoints animals need to be sacrificed.In this work, a systematic methodology for the development, analysis, identification (including sensitivity analysis, identifiabilityanalysis and parameter estimation) and discrimination between different pharmacokinetic candidate models is proposed. Themethodology is implemented into a computer-aided modeling framework. The benefit of the proposed systematic approach is animprovement of model quality and an increase of efficiency of the modeler as well as the experimentalist and thereby a reductionof time and resources required for model development, identification, discrimination and application. This is achieved by designingthe structure of the computer-aided modeling framework such that it can handle the work-flows and data-flows associated with thedifferent modeling tasks, combining state-of-the-art modeling techniques for the different work-flow steps as well as supportingmodel-documentation and model reuse.The proposed modeling framework for PBPK modeling is highlighted through a case study which deals with the development of aAbstract: Systematic Engineering Approach to Development and Identification of Physiologically-Based Pharmacokinetic Models (2011 Annual Meeting)http://aiche.confex.com/aiche/2011/webprogram/Paper236436.html[28-10-2011 15:41:55]PBPK model for a rat based on available experimental data.Extended Abstract: File Not UploadedSee more of this Session: Mathematical and Computational Biosystems Engineering See more of this Group/Topical: Computing and Systems Technology Division

Systematic Engineering Approach to Development and Identification of Physiologically-Based Pharmacokinetic Models

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Systematic Engineering Approach to Development and Identification of Physiologically-Based Pharmacokinetic Models

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