Successful Prediction of Human Hepatic Concentrations of Transported Drugs Using the Proteomics-Informed Relative Expression Factor Approach.

Tissue drug concentrations determine the efficacy and toxicity of drugs. When a drug is the substrate of transporters that are present at the blood:tissue barrier, the steady-state unbound tissue drug concentrations cannot be predicted from their corresponding plasma concentrations. To accurately predict transporter-modulated tissue drug concentrations, all clearances (CLs) mediating the drug's entry and exit (including metabolism) from the tissue must be accurately predicted. Since primary cells of most tissues are not available, we have proposed an alternative approach to predict such CLs, that is the use of transporter-expressing cells/vesicles (TECs/TEVs) and relative expression factor (REF). REF represents the abundance of the relevant transporters in the tissue vs. in the TECs/TEVs. Here, we determined the transporter-based intrinsic CL of glyburide (GLB) and pitavastatin (PTV) in OATP1B1, OATP1B3, OATP2B1, and NTCP-expressing cells and MRP3-, BCRP-, P-gp- and MRP2- expressing vesicles and scaled these CLs to in vivo using REF. These predictions fell within a priori set 2-fold range of the hepatobiliary CLs of GLB and PTV, estimated from their hepatic PET imaging data: 272.3 and 607.8 mL/min for in vivo hepatic sinusoidal uptake CL (CL s,uptake ), 47.8 and 17.4 mL/min for sinusoidal efflux CL (CL s,efflux ) and 0 and 4.20 mL/min for biliary efflux CL (CL bile ), respectively. Moreover, their predicted hepatic concentrations (AUC and C max ), fell within 2-fold of their mean observed data. These data, together with our previous findings, confirm that the REF approach can successfully predict transporter-based drug CLs and tissue concentrations to enhance success in drug development.