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

Abstract

BackgroundThe 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.ResultsIn 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.ConclusionsA 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.

Genetic Variation in the UGT1A Locus is Associated with Simvastatin Efficacy in a Clinical Practice Setting

“…However, this metabolic activation occurs in conjunction with variability in other key PK processes such as absorption, hepatic uptake and distribution that could also alter PD outcomes, a relationship that can be clarified by PK/PD modeling. Such models have been defined for statins and a dynamic systems biology modeling approach with statin bio-transformation by UGTs and CYPs has been used to explain interindividual differences in LDL-C lowering effects [41,42]. Models such as these could potentially be further refined by incorporating pharmacogenetic information.…”

A multi-route model of nicotine–cotinine pharmacokinetics, pharmacodynamics and brain nicotinic acetylcholine receptor binding in humans

“…The more recent utilization of PBPK models for evaluating the impact of human variability on internal dosimetry (Bois, 1999;Bois et al, 2010b;Marino et al, 2006;Nong et al, 2008;Yang et al, 2010) is of particular value for understanding nicotine pharmacokinetics; variability in metabolic and physiological parameters has been shown to contribute significantly to variability in drug and chemical dosimetry in blood and tissues (Bucher et al, 2011;Jack, 1985;Regardh, 1985;Routledge, 1985;Welling and Tse, 1984).…”

SBMLSimulator: A Java Tool for Model Simulation and Parameter Estimation in Systems Biology

“…Furthermore, after model simulation, the distance of the simulated data to the loaded experimental data is computed and displayed at the bottom of the SBMLsimulator window. [28]. SBMLsimulator enables the user to modify initial quantities (middle left part of window) and to choose the quantities for plotting (upper left).…”

“…In order to assess the capabilities of SBMLsimulator, we estimated the parameters of a model by Bucher et al [28] explaining the biotransformation of atorvastatin (model BIOMD0000000328 of BioModels Database [36]). We first obtained an artificial data set by simulating the model with the respective start and end time described in the publication.…”

“…Distribution of parameters estimated with SBMLsimulator. One hundred parameter estimations with SBMLsimulator for the model by Bucher et al [28] were run on a computer cluster. The distribution of the 50 estimations with the best fitness values is shown here.…”