2010 - Berlin - Germany

PAGE 2010: Methodology- PBPK
Jörg Lippert

Clinical trial simulation with multiscale models: Integrating whole-body physiology, disease biology, and molecular reaction networks

Thomas Eissing, Michael Block, Katrin Coboeken, Thomas Gaub, Lars Kuepfer, Michaela Meyer, Michael Sevestre, Juri Solodenko, Joerg Lippert

Systems Biology and Computational Solutions, Bayer Technology Services GmbH, Leverkusen, Germany

Objectives: Biology is multi-scale by nature. However, projects as well as software tools usually focus on isolated aspects of drug action such as pharmacokinetics at the whole-body scale or pharmacodynamic interaction on the molecular level. The objectives of this study are i) to introduce a software platform allowing for integrative modeling and simulation across biological scales, and ii) to illustrate the modeling concept realized in the platform by establishing a prototypical multiscale model for the progression of a pancreatic tumor in human patients and its treatment by pharmacotherapy.

Methods: The software platform consisting of PK-Sim® [1], MoBi® [2], and interfaces to R [3] and MATLAB® [4] offers both graphical user interfaces for model building and simulation as well as powerful command line functionalities for extended simulation and analysis tasks.

Results: Virtual patients are constructed and treated with an inactive prodrug that is activated by hepatic metabolization. Tumor growth in the model is driven by growth factor activation and MAPK signal transduction at the sub-cellular level [5]. Local free tumor concentrations of the active metabolite inhibit Raf kinase in the cascade and thereby cell cycle progression. In a virtual clinical study, the individual therapeutic outcome of drug therapy is simulated for a large virtual population with a reported heterogeneous genomic background [6,7]. The phenotype of the hepatic enzyme activating the prodrug has a strong impact on tumor progression under therapy. Oncogenic Ras mutations [8] influence tumor growth depending on the growth phase.

Conclusions: The application example presented demonstrates that efficient model building and integration of biological knowledge and prior data from all biological scales is feasible. The impact of events and processes at the molecular and organ level onto the physiology and pathophysiology of the whole organism can be readily studied by simulation. Experimental in vitro model systems can thereby be linked with observations in animal experiments and clinical trials. Thus, modern software tools solve the technical problem of model establishment and allow the application of multiscale modeling on demand, whenever business driven questions arise in the course of a project. Highly relevant clinical topics such as pharmacogenomics, drug-drug or drug-metabolite interactions can be studied based on a mechanistic, insight driven approach.

[1] Willmann S, Lippert J, Sevestre M, Solodenko J, Fois F, Schmitt W: PK-Sim®: a physiologically based pharmacokinetic ‘whole-body’ model. Biosilico 2003, 1(4):121-124.
[2] Bayer Technology Services GmbH, Leverkusen, Germany: Computational Systems Biology Products [www.systems-biology.com/products]
[3] R Foundation for Statistical Computing, Vienna, Austria: R - The R Project for Statistical Computing [http://www.r-project.org/]
[4] The MathWorks Inc., Natick, USA: MATLAB® - The Language Of Technical Computing [http://www.mathworks.com/products/matlab/]
[5] Brightman FA, Fell DA: Differential feedback regulation of the MAPK cascade underlies the quantitative differences in EGF and NGF signalling in PC12 cells. FEBS Lett 2000, 482(3):169-174.
[6] Zanger UM, Fischer J, Raimundo S, Stuven T, Evert BO, Schwab M, Eichelbaum M: Comprehensive analysis of the genetic factors determining expression and function of hepatic CYP2D6. Pharmacogenetics 2001, 11(7):573-585.
[7] Sistonen J, Sajantila A, Lao O, Corander J, Barbujani G, Fuselli S: CYP2D6 worldwide genetic variation shows high frequency of altered activity variants and no continental structure. Pharmacogenet Genomics 2007, 17(2):93-101. [8] Stites EC, Trampont PC, Ma Z, Ravichandran KS: Network analysis of oncogenic Ras activation in cancer. Science 2007, 318(5849):463-467.

Reference: PAGE 19 (2010) Abstr 1862 [www.page-meeting.org/?abstract=1862]
Poster: Methodology- PBPK