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Modeling of pharmacokinetics and pharmacodynamics
Drug discovery and development is still associated with high attrition rates. A recent analysis has shown that this high attrition is largely caused by lack of efficacy and unexpected safety concerns of new drugs. An important question is therefore how to improve the prediction of drug efficacy and safety.
This research program is a systems pharmacology approach which focuses on the development and application of novel mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modeling concepts. A pertinent feature of mechanism-based PK-PD models is that they contain expressions to describe, in a quantitative manner, processes on the causal path between plasma concentration and effect. To this end mechanism-based PK-PD modeling utilizes concepts from physiologically-based pharmacokinetic modeling, receptor theory, dynamical systems analysis and disease systems analysis (Figure 1). Mechanism-based PK-PD models have been and will be successfully employed for extrapolation and prediction in drug development.
Figure 1. Schematic representation of the systems pharmacology approach applied in mechanism based PK-PD modeling.
Full project title: Mechanism-based PK-PD modeling platform
Start date: October 2007
End date: October 2012
Goal: Develop a mechanism-based PK-PD model library together with a knowledge-based management system to securely store preclinical, clinical and epidemiological data and to support future model-based research in drug discovery and development
Principal investigator: Meindert Danhof
Project size: 17 FTE
Partners: AstraZeneca, Astellas, Eli Lilly, Erasmus Medical Center, GlaxoSmithKline, Johnson & Johnson PRD, Leiden University, Nycomed, Pfizer, MSD, University Medical Center Utrecht, University of Groningen
A unique feature of PK-PD modeling platform is that the mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) models are developed on the basis of existing data and that all partners have agreed to the sharing of data, models and biological system-specific information. To this end a novel and secure data management system will be established which houses the datasets from the academic and industrial partners enabling the project researchers to create mathematical models. The resulting mechanism-based PK-PD model library + databases of biological-system-specific information will be used to predict:
Martin Johnson (project D2-104)
Translational PKPD Modeling in Schizophrenia Predicting Human Receptor Occupancy
Imke Bartelink (project D2-104)
Individualization of drug exposure in pediatrichematopoietic stem cell transplantation
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