A computational systems biology software platform for multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks

被引:148
作者
Eissing, Thomas [1 ]
Kuepfer, Lars [1 ]
Becker, Corina [1 ]
Block, Michael [1 ]
Coboeken, Katrin [1 ]
Gaub, Thomas [1 ]
Goerlitz, Linus [1 ]
Jaeger, Juergen [1 ]
Loosen, Roland [1 ]
Ludewig, Bernd [1 ]
Meyer, Michaela [1 ]
Niederalt, Christoph [1 ]
Sevestre, Michael [1 ]
Siegmund, Hans-Ulrich [1 ]
Solodenko, Juri [1 ]
Thelen, Kirstin [1 ]
Telle, Ulrich [1 ]
Weiss, Wolfgang [1 ]
Wendl, Thomas [1 ]
Willmann, Stefan [1 ]
Lippert, Joerg [1 ]
机构
[1] Competence Ctr Syst Biol & Computat Solut, Bayer Technol Serv GmbH, D-51368 Leverkusen, Germany
关键词
systems biology; PBPK; software; multiscale; modeling; simulation; oncology; signal transduction; MARKUP LANGUAGE SBML; PHARMACOKINETIC MODELS; TISSUE DISTRIBUTION; IN-VIVO; TUMOR-GROWTH; VARIABILITY; PREDICTION; VOLUME; RISK; ABSORPTION;
D O I
10.3389/fphys.2011.00004
中图分类号
Q4 [生理学];
学科分类号
071003 ;
摘要
Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multiscale by nature, project work, and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform consisting of PK-Sim(R) and MoBi(R) capable of building and simulating models that integrate across biological scales. A prototypical multiscale model for the progression of a pancreatic tumor and its response to pharmacotherapy is constructed and virtual patients are treated with a prodrug activated by hepatic metabolization. Tumor growth is driven by signal transduction leading to cell cycle transition and proliferation. Free tumor concentrations of the active metabolite inhibit Raf kinase in the signaling cascade and thereby cell cycle progression. In a virtual clinical study, the individual therapeutic outcome of the chemotherapeutic intervention is simulated for a large population with heterogeneous genomic background. Thereby, the platform allows efficient model building and integration of biological knowledge and prior data from all biological scales. Experimental in vitro model systems can be linked with observations in animal experiments and clinical trials. The interplay between patients, diseases, and drugs and topics with high clinical relevance such as the role of pharmacogenomics, drug-drug, or drug-metabolite interactions can be addressed using this mechanistic, insight driven multiscale modeling approach.
引用
收藏
页数:10
相关论文
共 61 条
[31]  
2-P
[32]   Prediction of pharmacokinetics prior to in vivo studies.: II.: Generic physiologically based pharmacokinetic models of drug disposition [J].
Poulin, P ;
Theil, FP .
JOURNAL OF PHARMACEUTICAL SCIENCES, 2002, 91 (05) :1358-1370
[33]   Prediction of pharmacokinetics prior to in vivo studies.: 1.: Mechanism-based prediction of volume of distribution [J].
Poulin, P ;
Theil, FP .
JOURNAL OF PHARMACEUTICAL SCIENCES, 2002, 91 (01) :129-156
[34]   Comparing the growth kinetics of cell populations in two and three dimensions [J].
Radszuweit, M. ;
Block, M. ;
Hengstler, J. G. ;
Schoell, E. ;
Drasdo, D. .
PHYSICAL REVIEW E, 2009, 79 (05)
[35]   Cytochrome P450 2D6 activity predicts discontinuation of tamoxifen therapy in breast cancer patients [J].
Rae, J. M. ;
Sikora, M. J. ;
Henry, N. L. ;
Li, L. ;
Kim, S. ;
Oesterreich, S. ;
Skaar, T. C. ;
Nguyen, A. T. ;
Desta, Z. ;
Storniolo, A. M. ;
Flockhart, D. A. ;
Hayes, D. F. ;
Stearns, V. .
PHARMACOGENOMICS JOURNAL, 2009, 9 (04) :258-264
[36]   Control, exploitation and tolerance of intracellular noise [J].
Rao, CV ;
Wolf, DM ;
Arkin, AP .
NATURE, 2002, 420 (6912) :231-237
[37]   Physiologically based pharmacokinetic modeling 1: Predicting the tissue distribution of moderate-to-strong bases [J].
Rodgers, T ;
Leahy, D ;
Rowland, M .
JOURNAL OF PHARMACEUTICAL SCIENCES, 2005, 94 (06) :1259-1276
[38]   Tissue distribution of basic drugs:: Accounting for enantiomeric, compound and regional differences amongst β-blocking drugs in rat [J].
Rodgers, T ;
Leahy, D ;
Rowland, M .
JOURNAL OF PHARMACEUTICAL SCIENCES, 2005, 94 (06) :1237-1248
[39]   Mechanistic approaches to volume of distribution predictions: Understanding the processes [J].
Rodgers, Trudy ;
Rowland, Malcolm .
PHARMACEUTICAL RESEARCH, 2007, 24 (05) :918-933
[40]   Physiologically based pharmacokinetic modelling 2: Predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions [J].
Rodgers, Trudy ;
Rowland, Malcolm .
JOURNAL OF PHARMACEUTICAL SCIENCES, 2006, 95 (06) :1238-1257