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Open Access Highly Accessed Research article

A systems biology approach to dynamic modeling and inter-subject variability of statin pharmacokinetics in human hepatocytes

Joachim Bucher17, Stephan Riedmaier2, Anke Schnabel3, Katrin Marcus3, Gabriele Vacun18, Thomas S Weiss4, Wolfgang E Thasler5, Andreas K Nüssler6, Ulrich M Zanger2 and Matthias Reuss1*

Author affiliations

1 Institute of Biochemical Engineering, Allmandring 31, and Center Systems Biology, Nobelstraße 15, University of Stuttgart, 70569 Stuttgart, Germany

2 Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Auerbachstraße 112, 70376 Stuttgart, and University of Tübingen, 72074 Tübingen, Germany

3 Dep. Functional Proteomics, Medizinisches Proteom-Center, Ruhr-University Bochum, Universitätsstraße 150, 44801 Bochum, Germany

4 Department of Surgery, University of Regensburg, F.J.S. Allee 11, 93053 Regensburg, Germany

5 Department of Surgery, Ludwig-Maximilians-University, Marchioninistraße 15, 81377 München, Germany

6 Department of Traumatology, Technical University of Munich, MRI, Ismaningerstraße 22, 81675 Munich, Germany

7 Insilico Biotechnology AG, Meitnerstraße 8, 70563 Stuttgart, Germany

8 Fraunhofer Institut für Grenzflächen und Bioverfahrenstechnik, 70569 Stuttgart, Germany

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Citation and License

BMC Systems Biology 2011, 5:66  doi:10.1186/1752-0509-5-66

Published: 6 May 2011

Abstract

Background

The individual character of pharmacokinetics is of great importance in the risk assessment of new drug leads in pharmacological research. Amongst others, it is severely influenced by the properties and inter-individual variability of the enzymes and transporters of the drug detoxification system of the liver. Predicting individual drug biotransformation capacity requires quantitative and detailed models.

Results

In this contribution we present the de novo deterministic modeling of atorvastatin biotransformation based on comprehensive published knowledge on involved metabolic and transport pathways as well as physicochemical properties. The model was evaluated on primary human hepatocytes and parameter identifiability analysis was performed under multiple experimental constraints. Dynamic simulations of atorvastatin biotransformation considering the inter-individual variability of the two major involved enzymes CYP3A4 and UGT1A3 based on quantitative protein expression data in a large human liver bank (n = 150) highlighted the variability in the individual biotransformation profiles and therefore also points to the individuality of pharmacokinetics.

Conclusions

A dynamic model for the biotransformation of atorvastatin has been developed using quantitative metabolite measurements in primary human hepatocytes. The model comprises kinetics for transport processes and metabolic enzymes as well as population liver expression data allowing us to assess the impact of inter-individual variability of concentrations of key proteins. Application of computational tools for parameter sensitivity analysis enabled us to considerably improve the validity of the model and to create a consistent framework for precise computer-aided simulations in toxicology.