Nicola Melillo (1), Daniel Scotcher (1), Kayode Ogungbenro (1), J. Gerry Kenna (2), Claudia Green (3), Catherine D. G. Hines (4), Iina Laitinen (5), Paul D. Hockings (5), Steven Sourbron (6), John C. Waterton (2), Gunnar Schuetz (3), Aleksandra Galetin (1)
(1) Centre for Applied Pharmacokinetic Research, The University of Manchester, Manchester, UK; (2) Bioxydyn Limited, Manchester, UK; (3) MR and CT Contrast Media Research, Bayer AG, Berlin, Germany; (4) Merck & Co., Inc., Kenilworth, New Jersey, USA; (5) Antaros Medical, Mölndal, Sweden; (6) University of Sheffield, Sheffield, UK
Objectives. Physiologically-based pharmacokinetic (PBPK) models provide a framework for in vitro to in vivo extrapolation (IVIVE) of drug disposition. PBPK models are used in drug development to simulate changes in systemic and tissue exposure that arise due to changes in enzyme or transporter activity [1]. However, in humans quantitative prediction of transporter-mediated processes and tissue permeation remains challenging due to lack of available in vivo tissue data for model validation [2]. Gadoxetate is a magnetic resonance imaging (MRI) contrast agent used clinically for hepatic lesion characterization [3]. As a substrate of organic-anion-transporting polypeptide 1B1 (OATP1B1) and multidrug resistance-associated protein 2 (MRP2), gadoxetate is being explored as a novel imaging biomarker to support evaluation of drug-drug interactions (DDI) via these hepatic transporters [4]. The aims of this study are: to develop a gadoxetate PBPK model in rats, to explore the use of liver imaging data to inform and refine IVIVE of hepatic transporter kinetic data and to validate the PBPK model for prospective prediction of OATP1B DDI in rats.
Methods. Gadoxetate PBPK model was developed and the liver was described with a permeability limited model. Liver uptake and cellular binding were initially informed by the IVIVE (bottom-up analysis) of gadoxetate transporter kinetic data generated in plated rat hepatocytes. IVIVE was then refined by naïve pooled estimation of model parameters, including the active uptake from the liver extracellular tissue to the hepatocytes, CLactive, and the active excretion from the hepatocytes to the bile, CLbiliary (top-down analysis). In vivo dynamic contrast enhanced (DCE) MRI data of gadoxetate in rat blood, spleen and liver were used in this analysis. To test whether the model could reproduce the inhibitory phase, top-down analysis was performed using data from co-administration of gadoxetate and rifampicin 10 mg/kg. To assess the impact of the inclusion of liver data on IVIVE refinement, top-down analysis was performed considering only the blood data. Finally, the PBPK model was applied for prospective prediction of changes in gadoxetate systemic and liver AUC (AUCR) as a result of transporter modulation mediated by six drugs with varying degrees of OATP1B inhibition.
Results. PBPK-IVIVE of in vitro data predicted blood and spleen AUC up to 2.7-fold higher than the observed values, while CLactive and CLbiliary were ~10 and ~5 fold lower than the top-down values. Top-down CLactive and CLbiliary of gadoxetate control phase were equal to 2.17 (CV 11.5%) and 0.07 (CV 3.2%) L/h, while CLactive and CLbiliary estimates in the presence of rifampicin were equal to 0.95 (CV 16.1%) and 0.08 (CV 16.7%) L/h. Estimated CLactive inhibition by rifampicin was 96%. In the top-down analysis performed with only the blood data, CLactive for rifampicin phase was ~3-fold higher than the value obtained when liver profiles were used for parameter estimation and an inhibition of 84% was attributed to rifampicin inhibition of transporter activity. For the prospective prediction of OATP1B DDI, observed fold increase in gadoxetate systemic median (min-max) AUC were 1.94 (1.57-3.38) and 0.92 (0.84-1.6) for cyclosporine and rifampicin 2 mg/kg, respectively, while the predicted AUCR with these inhibitors were 3.38 and 1.15 respectively. Observed liver AUCR values for cyclosporine 0.26 (0.2-0.27) and rifampicin 0.68 (0.67-0.78) agreed with predicted AUCR values of 0.34 and 0.97. Remaining four drugs investigated (asunaprevir, bosentan, ketoconazole and pioglitazone) caused marginal changes in gadoxetate systemic and liver exposure; gadoxetate PBPK model correctly predicted no interactions with these drugs.
Conclusions. This study illustrates key role of liver imaging data for evaluation of predictive performance of prospective transporter IVIVE within PBPK modelling framework. Moreover, the liver data was essential in refining the gadoxetate transporter IVIVE to appropriately describe organ level concentration and to adequately characterize the magnitude of hepatic transporter DDI with rifampicin. The results of this work highlight that gadoxetate is a promising probe for quantification of the effect of perpetrator drugs on hepatic transporters (OATP1B and, potentially, MRP2) function in vivo. Work is ongoing to extend this approach in humans during drug development.
Acknowledgements. The research leading to these results received funding from the Innovative Medicines Initiatives 2 Joint Undertaking under grant agreement No 116106. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA.
References:[1] Zhang X, Yang Y, Grimstein M, et al (2020) Application of PBPK Modeling and Simulation for Regulatory Decision Making and Its Impact on US Prescribing Information: An Update on the 2018-2019 Submissions to the US FDA’s Office of Clinical Pharmacology. J Clin Pharmacol 60:S160–S178. https://doi.org/10.1002/jcph.1767
[2] Guo Y, Chu X, Parrott NJ, et al (2018) Advancing Predictions of Tissue and Intracellular Drug Concentrations Using In Vitro, Imaging and Physiologically Based Pharmacokinetic Modeling Approaches. Clin Pharmacol Ther 104:865–889. https://doi.org/10.1002/cpt.1183
[3] U.S. Food and Drug Administration (2018) US Food Drug Admin. Drug Labeling-Package Insert: EOVIST (Gadoxetate Disodium) Injection [FDA application no, (NDA) 022090. Accessed 20 Oct 2020
[4] Kenna JG, Waterton JC, Baudy A, et al (2018) Noninvasive Preclinical and Clinical Imaging of Liver Transporter Function Relevant to Drug-Induced Liver Injury. In: Chen M, Will Y (eds) Drug-Induced Liver Toxicity. Springer, New York, NY, pp 627–651
Reference: PAGE 29 (2021) Abstr 9724 [www.page-meeting.org/?abstract=9724]
Poster: Drug/Disease Modelling - Other Topics