ABSTRACT: Kinetic parameters describing hepatic uptake in hepatocytes are frequently estimated without appropriate incorporation of bidirectional passive diffusion, intracellular binding, and metabolism. A mechanistic two-compartment model was developed to describe all of the processes occurring during the in vitro uptake experiments performed in freshly isolated rat hepatocytes plated for 2 h. Uptake of rosuvastatin, pravastatin, pitavastatin, valsartan, bosentan, telmisartan, and repaglinide was investigated over a 0.1 to 300 ?M concentration range at 37°C for 2 or 45-90 min; nonspecific binding was taken into account. All concentration-time points were analyzed simultaneously by using a mechanistic two-compartment model describing uptake kinetics [unbound affinity constant (K(m,u)), maximum uptake rate (V(max)), unbound active uptake clearance (CL(active,u))], passive diffusion [unbound passive diffusion clearance (P(diff,u))], and intracellular binding [intracellular unbound fraction (fu(cell))]. When required (telmisartan and repaglinide), the model was extended to account for the metabolism [unbound metabolic clearance (CL(met,u))]. The CL(active,u) ranged 8-fold, reflecting a 11-fold range in uptake K(m,u), with telmisartan and valsartan showing the highest affinity for uptake transporters (K(m,u) <10 ?M). Both P(diff,u) and fu(cell) span over two orders of magnitude and reflected the lipophilicity of the drugs in the dataset. An extended incubation time allowed steady state to be reached between media and intracellular compartment concentrations and reduced the error in certain parameter estimates observed with shorter incubation times. Active transport accounted for >70% of total uptake for all drugs investigated and was 4- and 112-fold greater than CL(met,u) for telmisartan and repaglinide, respectively. Modeling of uptake kinetics in conjunction with metabolism improved the precision of the uptake parameter estimates for repaglinide and telmisartan. Recommendations are made for uptake experimental design and modeling strategies.
