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A Systems Biology Approach to Dynamic Modeling and Inter-Subject Variability of Statin Pharmacokinetics in Human Hepatocytes
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A Systems Biology Approach to Dynamic Modeling and Inter-Subject Variability of Statin Pharmacokinetics in Human Hepatocytes

A Systems Biology Approach to Dynamic Modeling and Inter-Subject Variability of Statin Pharmacokinetics in Human Hepatocytes
News

A Systems Biology Approach to Dynamic Modeling and Inter-Subject Variability of Statin Pharmacokinetics in Human Hepatocytes

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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.

The article is published online in BMC Systems Biology and is free to access.

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