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

Bucher J, Riedmaier S, Schnabel A, Marcus K, Vacun G, Weiss TS, Thasler WE, Nüssler AK, Zanger UM, Reuss M - BMC Syst Biol (2011)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Biochemical Engineering, Allmandring, and Center Systems Biology, Nobelstraße, University of Stuttgart, Germany.

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.

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Individual variability of CYP3A4 and UGT1A3 protein concentration level. Displayed is the distribution of CYP3A4 (top) and UGT1A3 (bottom) protein concentration data of individual human liver microsomes (n = 150). Protein concentrations are normalized to minimal value, respectively.
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Figure 6: Individual variability of CYP3A4 and UGT1A3 protein concentration level. Displayed is the distribution of CYP3A4 (top) and UGT1A3 (bottom) protein concentration data of individual human liver microsomes (n = 150). Protein concentrations are normalized to minimal value, respectively.

Mentions: Based on the model version of optimized parameters, gained in the simultaneous model fit, the effect of inter-individual variability of CYP3A4 and UGT1A3 protein expression levels was investigated by linking the protein expression data of 150 liver samples (Figure 6) via the described relative abundance approach in equation (11), using individual 1 as reference. UGT1A3 and CYP3A4 protein concentrations of individual 1 were converted from based on total protein amount to based on microsomal protein amount by multiplying with the factor 0.22, determined in human liver homogenates and corresponding microsome fractions via Bradford test [78].


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

Bucher J, Riedmaier S, Schnabel A, Marcus K, Vacun G, Weiss TS, Thasler WE, Nüssler AK, Zanger UM, Reuss M - BMC Syst Biol (2011)

Individual variability of CYP3A4 and UGT1A3 protein concentration level. Displayed is the distribution of CYP3A4 (top) and UGT1A3 (bottom) protein concentration data of individual human liver microsomes (n = 150). Protein concentrations are normalized to minimal value, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117731&req=5

Figure 6: Individual variability of CYP3A4 and UGT1A3 protein concentration level. Displayed is the distribution of CYP3A4 (top) and UGT1A3 (bottom) protein concentration data of individual human liver microsomes (n = 150). Protein concentrations are normalized to minimal value, respectively.
Mentions: Based on the model version of optimized parameters, gained in the simultaneous model fit, the effect of inter-individual variability of CYP3A4 and UGT1A3 protein expression levels was investigated by linking the protein expression data of 150 liver samples (Figure 6) via the described relative abundance approach in equation (11), using individual 1 as reference. UGT1A3 and CYP3A4 protein concentrations of individual 1 were converted from based on total protein amount to based on microsomal protein amount by multiplying with the factor 0.22, determined in human liver homogenates and corresponding microsome fractions via Bradford test [78].

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Biochemical Engineering, Allmandring, and Center Systems Biology, Nobelstraße, University of Stuttgart, Germany.

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.

Show MeSH