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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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Distributions from dynamic analysis of the Inter-individual variability in Atorvastatin metabolism. Distributions (bars) of AUC (top), cmax (middle) and of t(cmax) (bottom), and fitted probability density functions of simulated profiles of AS (solid lines) and of the sum of AS, ASpOH and ASoOH (dashed lines), respectively. Distributions are predicted by the dynamic simulation of Atorvastatin metabolism, implementing individual CYP3A4 and UGT1A3 protein concentration data (Figure 6).
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Figure 7: Distributions from dynamic analysis of the Inter-individual variability in Atorvastatin metabolism. Distributions (bars) of AUC (top), cmax (middle) and of t(cmax) (bottom), and fitted probability density functions of simulated profiles of AS (solid lines) and of the sum of AS, ASpOH and ASoOH (dashed lines), respectively. Distributions are predicted by the dynamic simulation of Atorvastatin metabolism, implementing individual CYP3A4 and UGT1A3 protein concentration data (Figure 6).

Mentions: The most important question that arises from this dynamic analysis was, how the metabolic profiles of the intracellular metabolites AS, ASpOH and ASoOH are influenced by this variability, since they are considered to be the active drugs, which inhibit HMGCoA-reductase [80]. Therefore, AUC, cmax and t(cmax) of the concentration-time-profiles of either AS alone or the sum of concentration of AS, ASpOH and ASoOH were calculated for each liver sample over a time period of 1200 min and the distributions over all liver samples were evaluated, respectively. Finally, appropriate probability density functions are fitted to the distributions (Figure 7). The probability density function characteristics are summarized in Table 6. Obviously, there are differences between the examination of only AS or the sum of concentrations of AS, ASpOH and ASoOH. AUC, cmax and t(cmax) have lower values in case of AS alone compared to the sum of all acidic metabolites. The population mean of AUC is 75051 (pmol ml-1 min) in the case of AS and 203617 (pmol ml-1 min) in the case of the sum of AS, ASpOH and ASoOH. Also, cmax is lower in the case of AS alone, 201 (pmol ml-1), compared to the sum of the acidic metabolites, 366 (pmol ml-1). Further, the maximal concentration appears at a shorter time point, 48 min, in the case of AS alone, compared to the time point, 100 min, of the sum of AS, ASpOH and ASoOH. The results are quite explainable, since ASpOH and ASoOH are the hydroxylated products of AS and therefore their maximal concentrations event at a delayed time-point compared to AS.


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)

Distributions from dynamic analysis of the Inter-individual variability in Atorvastatin metabolism. Distributions (bars) of AUC (top), cmax (middle) and of t(cmax) (bottom), and fitted probability density functions of simulated profiles of AS (solid lines) and of the sum of AS, ASpOH and ASoOH (dashed lines), respectively. Distributions are predicted by the dynamic simulation of Atorvastatin metabolism, implementing individual CYP3A4 and UGT1A3 protein concentration data (Figure 6).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Distributions from dynamic analysis of the Inter-individual variability in Atorvastatin metabolism. Distributions (bars) of AUC (top), cmax (middle) and of t(cmax) (bottom), and fitted probability density functions of simulated profiles of AS (solid lines) and of the sum of AS, ASpOH and ASoOH (dashed lines), respectively. Distributions are predicted by the dynamic simulation of Atorvastatin metabolism, implementing individual CYP3A4 and UGT1A3 protein concentration data (Figure 6).
Mentions: The most important question that arises from this dynamic analysis was, how the metabolic profiles of the intracellular metabolites AS, ASpOH and ASoOH are influenced by this variability, since they are considered to be the active drugs, which inhibit HMGCoA-reductase [80]. Therefore, AUC, cmax and t(cmax) of the concentration-time-profiles of either AS alone or the sum of concentration of AS, ASpOH and ASoOH were calculated for each liver sample over a time period of 1200 min and the distributions over all liver samples were evaluated, respectively. Finally, appropriate probability density functions are fitted to the distributions (Figure 7). The probability density function characteristics are summarized in Table 6. Obviously, there are differences between the examination of only AS or the sum of concentrations of AS, ASpOH and ASoOH. AUC, cmax and t(cmax) have lower values in case of AS alone compared to the sum of all acidic metabolites. The population mean of AUC is 75051 (pmol ml-1 min) in the case of AS and 203617 (pmol ml-1 min) in the case of the sum of AS, ASpOH and ASoOH. Also, cmax is lower in the case of AS alone, 201 (pmol ml-1), compared to the sum of the acidic metabolites, 366 (pmol ml-1). Further, the maximal concentration appears at a shorter time point, 48 min, in the case of AS alone, compared to the time point, 100 min, of the sum of AS, ASpOH and ASoOH. The results are quite explainable, since ASpOH and ASoOH are the hydroxylated products of AS and therefore their maximal concentrations event at a delayed time-point compared to AS.

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