Prediction of Hepatobiliary Clearances and Hepatic Concentrations of Transported Drugs in Humans Using Rosuvastatin as a Model Drug.

To assess efficacy and toxicity of a drug in humans, it is important to measure the tissue concentration of a drug at the target site. For a drug that is transported into or out of the tissue, the tissue unbound steady-state concentration can be dramatically different from its corresponding unbound steady-state plasma concentration. Because routine measurement of drug tissue concentrations is not possible, using rosuvastatin as a model transporter substrate drug, we compared the ability of the proteomics-informed relative expression factor (REF) approach and sandwich-cultured human hepatocytes (SCH) to accurately predict rosuvastatin human hepatobiliary clearances and hepatic concentrations. REF-predicted rosuvastatin biliary clearance (CL ), estimated using BCRP-overexpressing, MDR1-overexpressing, and MRP2-overexpressing vesicles, together with our previously published REF-predicted rosuvastatin hepatic sinusoidal uptake clearance (CL ) and physiologically scaled sinusoidal passive uptake and efflux clearance (CL ), were used to predict rosuvastatin hepatic concentrations. For SCH, the estimated rosuvastatin CL , CL , and CL were scaled using physiological scaling. The REF-predicted CL (6.39 ± 1.56 mL/minute) and hepatic rosuvastatin area under the concentration-time curve (AUC) fell within our a priori defined success criterion, i.e., within twofold of the observed positron emission tomography-imaged values. In contrast, as expected, SCH dramatically overpredicted (predicted/observed ratio P/O = 8.38-10.41) rosuvastatin CL , and underpredicted hepatic AUC (P/O = 0.08-0.14). For both approaches, predictions were improved by using the parallel tube model vs. well-stirred model. Overall, using rosuvastatin as a model drug, this study demonstrates the success of the REF approach in predicting in vivo CL and hepatic concentration of drugs, and highlights the shortcomings of the SCH approach in making such predictions.