Abstract
Physiologically based pharmacokinetic (PBPK) models are increasingly used in drug development to simulate changes in both systemic and tissue exposures that arise as a result of changes in enzyme and/or transporter activity. Verification of these model-based simulations of tissue exposure is challenging in the case of transporter-mediated drug–drug interactions (tDDI), in particular as these may lead to differential effects on substrate exposure in plasma and tissues/organs of interest. Gadoxetate, a promising magnetic resonance imaging (MRI) contrast agent, is a substrate of organic-anion-transporting polypeptide 1B1 (OATP1B1) and multidrug resistance-associated protein 2 (MRP2). In this study, we developed a gadoxetate PBPK model and explored the use of liver-imaging data to achieve and refine in vitro–in vivo extrapolation (IVIVE) of gadoxetate hepatic transporter kinetic data. In addition, PBPK modeling was used to investigate gadoxetate hepatic tDDI with rifampicin i.v. 10 mg/kg. In vivo dynamic contrast-enhanced (DCE) MRI data of gadoxetate in rat blood, spleen, and liver were used in this analysis. Gadoxetate in vitro uptake kinetic data were generated in plated rat hepatocytes. Mean (%CV) in vitro hepatocyte uptake unbound Michaelis–Menten constant (Km,u) of gadoxetate was 106 μM (17%) (n = 4 rats), and active saturable uptake accounted for 94% of total uptake into hepatocytes. PBPK–IVIVE of these data (bottom-up approach) captured reasonably systemic exposure, but underestimated the in vivo gadoxetate DCE–MRI profiles and elimination from the liver. Therefore, in vivo rat DCE–MRI liver data were subsequently used to refine gadoxetate transporter kinetic parameters in the PBPK model (top-down approach). Active uptake into the hepatocytes refined by the liver-imaging data was one order of magnitude higher than the one predicted by the IVIVE approach. Finally, the PBPK model was fitted to the gadoxetate DCE–MRI data (blood, spleen, and liver) obtained with and without coadministered rifampicin. Rifampicin was estimated to inhibit active uptake transport of gadoxetate into the liver by 96%. The current analysis highlighted the importance of gadoxetate liver data for PBPK model refinement, which was not feasible when using the blood data alone, as is common in PBPK modeling applications. The results of our study demonstrate the utility of organ-imaging data in evaluating and refining PBPK transporter IVIVE to support the subsequent model use for quantitative evaluation of hepatic tDDI.
Highlights
The physiologically based pharmacokinetic (PBPK) modeling approach provides an effective mechanistic framework for quantitative translation of pharmacokinetic (PK) data
Gadoxetate has been proposed as a potential imaging biomarker for evaluation of drug−drug interactions (DDI) mediated by OATP1B1 and multidrug resistance-associated protein 2 (MRP2).12,13,57 In this work, a PBPK model for the magnetic resonance imaging (MRI) contrast agent gadoxetate was developed to enable characterization of liver transporter DDI and to explore the use of liverimaging data to achieve and refine hepatic transporter in vivo extrapolation (IVIVE)
The current study aimed to develop and evaluate the PBPK model for gadoxetate; prospective prediction of the gadoxetate−rifampicin interaction was not performed due to uncertainties associated with IVIVE of in vitro inhibition data and complexities of substratedependent inhibition associated with OATP1B1.58 Application of the PBPK modeling for quantitative and translational prediction of gadoxetate−drug interactions will be explored in future work with an extended dataset of transporter inhibitors
Summary
The physiologically based pharmacokinetic (PBPK) modeling approach provides an effective mechanistic framework for quantitative translation of pharmacokinetic (PK) data. Direct measurement of in vivo drug concentration−time data in specific tissues of interest is practically and ethically challenging.[4] an understanding of these local concentrations (total and unbound) can aid the delineation of sources of variability in drug response, for which measurements of drug concentrations in plasma may be insufficient.[4,6,9] For drugs predominantly eliminated by liver, perturbations of efflux transporters relevant for their biliary excretion may lead to clinically relevant changes in liver exposure, which may not be reflected in the systemic concentrations (depending on rate-limiting processes).[4,10,11] In this context, PBPK model-based predictions of local drug concentrations represent a useful surrogate, yet verifying key assumptions of model structure and parameter values (e.g., efflux clearances) solely from plasma clinical data is challenging
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
