ITC recommendations for transporter kinetic parameter estimation and translational modeling of transport-mediated PK and DDIs in humans

Abstract

This white paper provides a critical analysis of methods for estimating transporter kinetics and recommendations on proper parameter calculation in various experimental systems. Rational interpretation of transporter-knockout animal findings and application of static and dynamic physiologically based modeling approaches for prediction of human transporter-mediated pharmacokinetics and drug-drug interactions (DDIs) are presented. The objective is to provide appropriate guidance for the use of in vitro, in vivo, and modeling tools in translational transporter science.

Figure 1

Diagrammatic representations of (a, model I) the simplified three-compartment monolayer efflux model, (b, model II) the five-compartment model containing lipid partitioning compartments at the basolateral and apical membranes (it is capable of accounting for lag time in monolayer flux), and (c, model III) the structural monolayer efflux model. C_Lact,efflux, active efflux clearance; CL_diff, passive diffusion unbound clearance; C_cell,u, unbound cellular concentration; CL_i, diffusional unbound clearance into membrane; CL_o, diffusional unbound clearance out of membrane; C_membrane,u, unbound membrane concentration; k₁, substrate binding on rate; k₋₁, substrate binding off rate; k₂, substrate efflux rate constant to apical compartment; P-gp, P-glycoprotein; TJ, tight junction.

Figure 2

Relationship between change in exposure in a knockout animal vs. wild-type control and the fraction excreted (f_e) by the knocked-out transporter at different inhibitor concentrations: I/K_i = 1 (dotted line), 2 (dotted-dashed line), 5 (short-dashed line), 10 (long-dashed line), and ∞ (genetic ablation; solid line). Exposure refers to systemic or tissue exposure, as well as recovery in any particular excreta.

Figure 3

Permeability-limited liver model depicting the existence of a diffusional barrier at the cell membrane and processes occurring in extracellular and intracellular compartments. CL_act,uptake, CL_act,efflux, and CL_diff represent clearances via active uptake, sinusoidal efflux, and passive diffusion, respectively. CL_int,met and CL_int,bile represent loss of drug due to metabolism and biliary excretion, respectively. f_u,p, f_u,ISF, and f_u,cell represent fraction unbound in plasma, interstitial fluid, and liver cell, respectively. Q_H, C_b, and C_T represent hepatic blood flow, unbound blood, and tissue concentration, respectively.

Figure 4

PBPK prediction of transporter-mediated DDIs. (a) PBPK-model simulated dynamic reduction of hepatic uptake transporter activity (solid line) and constant interaction potential based on static calculation (dashed line) by cyclosporine over time. (b) PBPK-simulated dynamic reduction of hepatic OATP1B1 activity by the parent (solid line) and when metabolite is also included in the assessment (dashed/dotted lines). Metabolite exposure is either 25% (dashed lines) or 100% (dotted lines) of the parent exposure. Assumptions: equipotent OATP1B1 inhibition by parent and metabolite, the same plasma f_u for parent and metabolite, and competitive OATP1B1 inhibition. (c) All conditions as described in b, except metabolite is assumed to be a 10-fold more potent OATP1B1 inhibitor than the parent. OATP, organic anion-transporting polypeptide; PBPK, physiologically based pharmacokinetics.