2010 - Berlin - Germany

PAGE 2010: Methodology- PBPK
Jörg Lippert

Using relative gene expression measurements for PBPK modeling of pravastatin

M. Meyer, L. Kuepfer, D. Chandra Prakash, S. Schneckener, B. Ludewig, J. Lippert

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

Objectives: Passive distribution of a drug in the body largely depends on the physicochemical properties of the compound, whereas both metabolization and active transport are dependent on the availability of enzymes or transporters and occur simultaneously in different tissues. A quantitative description of these processes, however, is difficult due to a limited experimental accessibility of tissue-specific protein activity in vivo. Here, we propose a novel method for the incorporation of expression data as a surrogate for protein activity into physiologically-based pharmacokinetic (PBPK) models. The general feasibility of this approach is demonstrated in a case study of pravastatin PK model building. The resulting model contains a reduced number of free parameters and presents a physiological description of the molecular mechanisms underlying metabolism and distribution.

Methods: To evaluate the incorporation of expression measurements in PBPK modeling, three publicly available sources were considered for building a PBPK model of pravastatin: EST counts from Unigene [1], expression data from ArrayExpress [2], and RT-PCR results from literature [3-5]. Expression data from these databases were used to build three independently parameterized models, which include the active processes relevant for pravastatin pharmacokinetics [6, 7]: uptake by OATP1B1 in the liver, transport by MRP2 in intestine, liver and kidney, and metabolization by sulfotransferases (SULTs) in intestine, liver and kidney. The PBPK model for pravastatin was built using the software tools PK-Sim® and MoBi® [8, 9]. The models for single dose iv and po administration were adjusted to experimental plasma concentrations and urinary secretion data [10-12].

Results: Incorporation of gene expression data from three independent sources (Unigene, ArrayExpress, and literature) into a PBPK model of pravastatin yielded plasma concentration curves that showed a good agreement with experimental PK data reported in humans. Depending on the relative distribution of either transporters or enzymes in different tissues, slight differences in the plasma concentration-time profiles were observed.

Conclusions: The new approach using relative expression measurements to reduce the number of free parameters in a PBPK model combines top-down pharmacokinetic modeling at the whole-body level with experimental measurements at the tissue scale. It supports mechanistic analyses of clinical studies and their design.

[1] National Center for Biotechnology Information (NCBI) - Unigene. http://www.ncbi.nlm.nih.gov/unigene.
[2] European Informatics Institute (EBI) - ArrayExpress. http://www.ebi.ac.uk/microarray-as/ae/.
[3] Nishimura, M., and S. Naito. 2005. Tissue-specific mRNA expression profiles of human ATP-binding cassette and solute carrier transporter superfamilies. Drug Metab Pharmacokinet 20: 452-477.
[4] Nishimura, M., H. Yaguti, H. Yoshitsugu, S. Naito, and T. Satoh. 2003. Tissue distribution of mRNA expression of human cytochrome P450 isoforms assessed by high-sensitivity real-time reverse transcription PCR. Yakugaku Zasshi 123: 369-375.
[5] Nishimura, M., and S. Naito. 2006. Tissue-specific mRNA expression profiles of human phase I metabolizing enzymes except for cytochrome P450 and phase II metabolizing enzymes. Drug Metab Pharmacokinet 21: 357-374.
[6] Hatanaka, T. 2000. Clinical pharmacokinetics of pravastatin: mechanisms of pharmacokinetic events. Clin Pharmacokinet 39: 397-412.
[7] Kivisto, K. T., and M. Niemi. 2007. Influence of drug transporter polymorphisms on pravastatin pharmacokinetics in humans. Pharm Res 24: 239-247.
[8] Willmann, S., J. Lippert, M. Sevestre, J. Solodenko, F. Fois, and W. Schmitt. 2003. PK-Sim©: a physiologically based pharmacokinetic 'whole-body' model. Biosilico 1: 121-124.
[9] Bayer Technology Services GmbH - MoBi®: The systems biology software tool for multiscale physiological modeling and simulation.: http://www.systems-biology.com/products/mobi.html.
[10] Singhvi, S. M., H. Y. Pan, R. A. Morrison, and D. A. Willard. 1990. Disposition of pravastatin sodium, a tissue-selective HMG-CoA reductase inhibitor, in healthy subjects. Br J Clin Pharmacol 29: 239-243.
[11] Everett, D. W., T. J. Chando, G. C. Didonato, S. M. Singhvi, H. Y. Pan, and S. H. Weinstein. 1991. Biotransformation of pravastatin sodium in humans. Drug Metab Dispos 19: 740-748.
[12] Bauer, S., J. Mwinyi, A. Stoeckle, T. Gerloff, and I. Roots. 2005. Quantification of pravastatin in human plasma and urine after solid phase extraction using high performance liquid chromatography with ultraviolet detection. J Chromatogr B Analyt Technol Biomed Life Sci 818: 257-262.

Reference: PAGE 19 (2010) Abstr 1868 [www.page-meeting.org/?abstract=1868]
Poster: Methodology- PBPK