Hepatic OATP1B zonal distribution: Implications for rifampicin-mediated drug-drug interactions explored within a PBPK framework.

OATP1B facilitates the uptake of xenobiotics into hepatocytes and is a prominent target for drug-drug interactions (DDIs). Reduced systemic exposure of OATP1B substrates has been reported following multiple-dose rifampicin; one explanation for this observation is OATP1B induction. Non-uniform hepatic distribution of OATP1B may impact local rifampicin tissue concentrations and rifampicin-mediated protein induction, which may affect the accuracy of transporter- and/or metabolizing enzyme-mediated DDI predictions. We incorporated quantitative zonal OATP1B distribution data from immunofluorescence imaging into a PBPK modeling framework to explore rifampicin interactions with OATP1B and CYP substrates. PBPK models were developed for rifampicin, two OATP1B substrates, pravastatin and repaglinide (also metabolized by CYP2C8/CYP3A4), and the CYP3A probe, midazolam. Simulated hepatic uptake of pravastatin and repaglinide increased from the periportal to the pericentral region (approximately 2.1-fold), consistent with OATP1B distribution data. Simulated rifampicin unbound intracellular concentrations increased in the pericentral region (1.64-fold) compared to simulations with uniformly distributed OATP1B. The absolute average fold error of the rifampicin PBPK model for predicting substrate maximal concentration (Cmax) and area under the plasma concentration-time curve (AUC) ratios was 1.41 and 1.54, respectively (nine studies). In conclusion, hepatic OATP1B distribution has a considerable impact on simulated zonal substrate uptake clearance values and simulated intracellular perpetrator concentrations, which regulate transporter and metabolic DDIs. Additionally, accounting for rifampicin-mediated OATP1B induction in parallel with inhibition improved model predictions. This study provides novel insight into the effect of hepatic OATP1B distribution on site-specific DDI predictions and the impact of accounting for zonal transporter distributions within PBPK models.

Non-specific binding of compounds in in vitro metabolism assays: a comparison of microsomal and hepatocyte binding in different species and an assessment of the accuracy of prediction models.

Non-specific binding in in vitro metabolism systems leads to an underestimation of the true intrinsic metabolic clearance of compounds being studied. Therefore in vitro binding needs to be accounted for when extrapolating in vitro data to predict the in vivo metabolic clearance of a compound. While techniques exist for experimentally determining the fraction of a compound unbound in in vitro metabolism systems, early in drug discovery programmes computational approaches are often used to estimate the binding in the in vitro system.Experimental fraction unbound data (n = 60) were generated in liver microsomes (fumic) from five commonly used pre-clinical species (rat, mouse, dog, minipig, monkey) and humans. Unbound fraction in incubations with mouse, rat or human hepatocytes was determined for the same 60 compounds. These data were analysed to determine the relationship between experimentally determined binding in the different matrices and across different species. In hepatocytes there was a good correlation between fraction unbound in human and rat (r2=0.86) or mouse (r2=0.82) hepatocytes. Similar correlations were observed between binding in human liver microsomes and microsomes from rat, mouse, dog, Göttingen minipig or monkey liver microsomes (r2 of >0.89, n = 51 - 52 measurements in different species). Physicochemical parameters (logP, pKa and logD) were predicted for all evaluated compounds. In addition, logP and/or logD were measured for a subset of compounds.Binding to human hepatocytes predicted using 5 different methods was compared to the measured data for a set of 59 compounds. The best methods evaluated used measured microsomal binding in human liver microsomes to predict hepatocyte binding. The collated physicochemical data were used to predict the human fumic using four different in silico models for a set of 53-60 compounds. The correlation (r2) and root mean square error between predicted and observed microsomal binding was 0.69 & 0.20, 0.47 & 0.23, 0.56 & 0.21 and 0.54 & 0.26 for the Turner-Simcyp, Austin, Hallifax-Houston and Poulin models, respectively. These analyses were extended to include measured literature values for binding in human liver microsomes for a larger set of compounds (n=697). For the larger dataset of compounds, microsomal binding was well predicted for neutral compounds (r2=0.67 - 0.70) using the Poulin, Austin, or Turner-Simcyp methods but not for acidic or basic compounds (r2<0.5) using any of the models. While the lipophilicity-based models can be used, the in vitro binding should be measured for compounds where more certainty is needed, using appropriately calibrated assays and possibly established weak, moderate, and strong binders as reference compounds to allow comparison across databases.

Physiologically-Based Pharmacokinetic Models of CYP2D6 Substrate and Inhibitors Nebivolol, Cinacalcet and Mirabegron to Simulate Drug-Drug Interactions.

BACKGROUND AND OBJECTIVES: Index substrates and inhibitors to investigate the role of the polymorphic enzyme, cytochrome P450 (CYP) 2D6, in the metabolism of new compounds have been proposed by regulatory agencies. This work describes the development and verification of physiologically-based pharmacokinetic (PBPK) models for the CYP2D6-sensitive substrate, nebivolol and the index CYP2D6 inhibitors, mirabegron and cinacalcet. METHODS: PBPK models for nebivolol, mirabegron and cinacalcet were developed using in vitro and clinical data. The performance of the PBPK models was verified by comparing the simulated results against reported human systemic exposure and clinical drug-drug interactions (DDIs) studies. RESULTS: The exposure of nebivolol, cinacalcet and mirabegron predicted by the PBPK models was verified against pharmacokinetic data from 13, 3 and 9 clinical studies, respectively. For nebivolol, the predicted mean maximum plasma concentration (Cmax) and area under the plasma concentration-time (AUC) values in CYP2D6 extensive metaboliser subjects were within 0.9- to 1.49-fold of the observed values. In poor metaboliser CYP2D6 subjects, the predicted Cmax and AUC values were within 0.41- to 0.81-fold of observed values. For cinacalcet, the predicted Cmax and AUC values were within 0.97- to 1.32-fold of the observed data. For mirabegron, the predicted AUC values across all the studies investigated were within 0.71- to 1.88-fold of observed values. The PBPK model-predicted DDIs were in good agreement (within 2-fold) with observed DDIs in all verification studies (n = 8) assessed. The overall precision was 1.26 and 1.21 for Cmax and the AUC ratio, respectively. CONCLUSIONS: The developed PBPK models can be used to assess the DDI potential liability of new chemical entities that are substrates or inhibitors of CYP2D6.