When Does the Rate-Determining Step in the Hepatic Clearance of a Drug Switch from Sinusoidal Uptake to All Hepatobiliary Clearances? Implications for Predicting Drug-Drug Interactions

Building a Predictive PBPK Model for Human OATP Substrates: a Strategic Framework for Early Evaluation of Clinical Pharmacokinetic Variations Using Pitavastatin as an Example

To select a drug candidate for clinical development, accurately and promptly predicting human pharmacokinetic (PK) profiles, assessing drug-drug interactions (DDIs), and anticipating potential PK variations in disease populations are crucial steps in drug discovery. The complexity of predicting human PK significantly increases when hepatic transporters are involved in drug clearance (CL) and volume of distribution (Vss). A strategic framework is developed here, utilizing pitavastatin as an example. The framework includes the construction of a monkey physiologically-based PK (PBPK) model, model calibration to obtain scaling factors (SF) of in vitro-in vivo extrapolation (IVIVE) for various clearance parameters, human model development and validation, and assessment of DDIs and PK variations in disease populations. Through incorporating in vitro human parameters and calibrated SFs from the monkey model of 3.45, 0.14, and 1.17 for CLint,active, CLint,passive, and CLint,bile, respectively, and together with the relative fraction transported by individual transporters obtained from in vitro studies and the optimized Ki values for OATP inhibition, the model reasonably captured observed pitavastatin PK profiles, DDIs and PK variations in human subjects carrying genetic polymorphisms, i.e., AUC within 20%. Lastly, when applying the functional reduction based on measured OATP1B biomarkers, the model adequately predicted PK changes in the hepatic impairment population. The present study presents a strategic framework for early-stage drug development, enabling the prediction of PK profiles and assessment of PK variations in scenarios like DDIs, genetic polymorphism, and hepatic impairment-related disease states, specifically focusing on OATP substrates.

Toward improved predictions of pharmacokinetics of transported drugs in hepatic impairment: Insights from the extended clearance model

Hepatic impairment (HI) moderately (<5-fold) affects the systemic exposure (i.e., area under the plasma concentration-time curve [AUC]) of drugs that are substrates of the hepatic sinusoidal organic anion transporting polypeptide (OATP) transporters and are excreted unchanged in the bile and/or urine. However, the effect of HI on their AUC is much greater (>10-fold) for drugs that are also substrates of cytochrome P450 (CYP) 3A enzymes. Using the extended clearance model, through simulations, we identified the ratio of sinusoidal efflux clearance (CL) over the sum of metabolic and biliary CLs as important in predicting the impact of HI on the AUC of dual OATP/CYP3A substrates. Because HI may reduce hepatic CYP3A-mediated CL to a greater extent than biliary efflux CL, the greater the contribution of the former versus the latter, the greater the impact of HI on drug AUC ratio (AUCRHI ). Using physiologically-based pharmacokinetic modeling and simulation, we predicted relatively well the AUCRHI of OATP substrates that are not significantly metabolized (pitavastatin, rosuvastatin, valsartan, and gadoxetic acid). However, there was a trend toward underprediction of the AUCRHI of the dual OATP/CYP3A4 substrates fimasartan and atorvastatin. These predictions improved when the sinusoidal efflux CL of these two drugs was increased in healthy volunteers (i.e., before incorporating the effect of HI), and by modifying the directionality of its modulation by HI (i.e., increase or decrease). To accurately predict the effect of HI on AUC of hepatobiliary cleared drugs it is important to accurately predict all hepatobiliary pathways, including sinusoidal efflux CL.

Biomarker-Informed Model-Based Risk Assessment of Organic Anion Transporting Polypeptide 1B Mediated Drug-Drug Interactions

Quantitative prediction of drug-drug interactions (DDIs) involving organic anion transporting polypeptide (OATP)1B1/1B3 inhibition is limited by uncertainty in the translatability of experimentally determined in vitro inhibition potency (half-maximal inhibitory concentration (IC₅₀ )). This study used an OATP1B endogenous biomarker-informed physiologically-based pharmacokinetic (PBPK) modeling approach to predict the effect of inhibitor drugs on the pharmacokinetics (PKs) of OATP1B substrates. Initial static analysis with about 42 inhibitor drugs, using in vitro IC₅₀ values and unbound liver inlet concentrations (I_in,max,u ), suggested in vivo OATP1B inhibition risk for drugs with R-value (1+ I_in,max,u /IC₅₀ ) above 1.5. A full-PBPK model accounting for transporter-mediated hepatic disposition was developed for coproporphyrin I (CP-I), an endogenous OATP1B biomarker. For several inhibitors (cyclosporine, diltiazem, fenebrutinib, GDC-0810, itraconazole, probenecid, and rifampicin at 3 different doses), PBPK models were developed and verified against available CP-I plasma exposure data to obtain in vivo OATP1B inhibition potency-which tend to be lower than the experimentally measured in vitro IC₅₀ by about 2-fold (probenecid and rifampicin) to 37-fold (GDC-0810). Models verified with CP-I data are subsequently used to predict DDIs with OATP1B probe drugs, rosuvastatin and pitavastatin. The predicted and observed area under the plasma concentration-time curve ratios are within 20% error in 55% cases, and within 30% error in 89% cases. Collectively, this comprehensive study illustrates the adequacy and utility of endogenous biomarker-informed PBPK modeling in mechanistic understanding and quantitative predictions of OATP1B-mediated DDIs in drug development.

Quantitative prediction of breast cancer resistant protein mediated drug-drug interactions using physiologically-based pharmacokinetic modeling

Quantitative assessment of drug‐drug interactions (DDIs) involving breast cancer resistance protein (BCRP) inhibition is challenged by overlapping substrate/inhibitor specificity. This study used physiologically‐based pharmacokinetic (PBPK) modeling to delineate the effects of inhibitor drugs on BCRP‐ and organic anion transporting polypeptide (OATP)1B‐mediated disposition of rosuvastatin, which is a recommended BCRP clinical probe. Initial static model analysis using in vitro inhibition data suggested BCRP/OATP1B DDI risk while considering regulatory cutoff criteria for a majority of inhibitors assessed (25 of 27), which increased rosuvastatin plasma exposure to varying degree (~ 0–600%). However, rosuvastatin area under plasma concentration‐time curve (AUC) was minimally impacted by BCRP inhibitors with calculated G‐value (= gut concentration/inhibition potency) below 100. A comprehensive PBPK model accounting for intestinal (OATP2B1 and BCRP), hepatic (OATP1B, BCRP, and MRP4), and renal (OAT3) transport mechanisms was developed for rosuvastatin. Adopting in vitro inhibition data, rosuvastatin plasma AUC changes were predicted within 25% error for 9 of 12 inhibitors evaluated via PBPK modeling. This study illustrates the adequacy and utility of a mechanistic model‐informed approach in quantitatively assessing BCRP‐mediated DDIs.

Recent developments in in vitro and in vivo models for improved translation of preclinical pharmacokinetics and pharmacodynamics data

Improved pharmacokinetics/pharmacodynamics (PK/PD) prediction in the early stages of drug development is essential to inform lead optimization strategies and reduce attrition rates. Recently, there have been significant advancements in the development of new in vitro and in vivo strategies to better characterize pharmacokinetic properties and efficacy of drug leads. Herein, we review advances in experimental and mathematical models for clearance predictions, advancements in developing novel tools to capture slowly metabolized drugs, in vivo model developments to capture human etiology for supporting drug development, limitations and gaps in these efforts, and a perspective on the future in the field.

Prediction of Transporter-Mediated Rosuvastatin Hepatic Uptake Clearance and Drug Interaction in Humans Using Proteomics-Informed REF Approach

Suspended, plated, or sandwich-cultured human hepatocytes are routinely used for in vitro to in vivo extrapolation (IVIVE) of transporter-mediated hepatic clearance (CL) of drugs. However, these hepatocyte models have been reported to underpredict transporter-mediated in vivo hepatic uptake CL (CL _{uptake,in vivo} ) of some drugs. Therefore, we determined whether transporter-expressing cells (TECs) can accurately predict the CL _{uptake,in vivo} of drugs. To do so, we determined the uptake CL (CL _{int,uptake,cells} ) of rosuvastatin (RSV) by TECs (organic anion transporting polypeptides/Na⁺-taurocholate cotransporting polypeptide) and then scaled it to that in vivo by relative expression factor (REF) (the ratio of transporter abundance in human livers and TEC) determined by liquid chromatography tandem mass spectrometry-based quantitative proteomics. Both the TEC and hepatocyte models did not meet our predefined success criteria of predicting within 2-fold the RSV CL _{uptake,in vivo} value obtained from our positron emission tomography (PET) imaging. However, the TEC performed better than the hepatocyte models. Interestingly, using REF, TECs successfully predicted RSV CL _{int,uptake,hep} obtained by the hepatocyte models, suggesting that the underprediction of RSV CL _{uptake,in vivo} by TECs and hepatocytes is due to endogenous factor(s) not present in these in vitro models. Therefore, we determined whether inclusion of plasma (or albumin) in TEC uptake studies improved IVIVE of RSV CL _{uptake,in vivo} It did, and our predictions were close to or just fell above our lower 2-fold acceptance boundary. Despite this success, additional studies are needed to improve transporter-mediated IVIVE of hepatic uptake CL of drugs. However, using REF and TEC, we successfully predicted the magnitude of PET-imaged inhibition of RSV CL _{uptake,in vivo} by cyclosporine A. SIGNIFICANCE STATEMENT: We showed that the in vivo transporter-mediated hepatic uptake CL of rosuvastatin, determined by PET imaging, can be predicted (within 2-fold) from in vitro studies in transporter-expressing cells (TECs) (scaled using REF), but only when plasma proteins were included in the in vitro studies. This conclusion did not hold when plasma proteins were absent in the TEC or human hepatocyte studies. Thus, additional studies are needed to improve in vitro to in vivo extrapolation of transporter-mediated drug CL.

Absence of OATP1B (Organic Anion-Transporting Polypeptide) Induction by Rifampin in Cynomolgus Monkeys: Determination Using the Endogenous OATP1B Marker Coproporphyrin and Tissue Gene Expression

Organic anion-transporting polypeptide (OATP) 1B induction is an evolving mechanism of drug disposition and interaction. However, there are contradictory reports describing OATP1B expression in hepatocytes and liver biopsies after administration of an inducer. This study investigated the in vivo effects of the common inducer rifampin (RIF) on the activity and expression of cynomolgus monkey OATP1B1 and OATP1B3 transporters, which are structurally and functionally similar their human OATP1B counterparts. Multiple doses of oral RIF (15 mg/kg) resulted in a steady 3.9-fold increase of CYP3A biomarker, 4β-hydroxycholesterol (4βHC), in the plasma samples collected before each RIF dose during the treatment period (i.e., predose). In contrast, the predose plasma levels of OATP1B biomarkers coproporphyrin (CP) I and CPIII did not change when compared with RIF treatment. The trough concentration, area under plasma concentration-time curve (AUC), and half-life of RIF decreased markedly during RIF treatment, suggesting that RIF induced its own clearance. Consequently, RIF treatment increased CPI and CPIII AUCs substantially after a single administration and, to a lesser extent, after multiple administrations compared with preadministration AUCs. In addition, OATP1B1 and OATP1B3 mRNA expressions were not modulated by RIF treatment (0.85-1.3-fold), whereas CYP3A8 expression was increased 3.7-5.0-fold, which correlated well with the predose levels of CP and 4βHC. Rifampin treatment showed 2.0-3.3-fold increases in P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), and multidrug resistance-associated protein 2 (MRP2) expression in the small intestine. Collectively, these findings indicate that monkey OATP1B and OATP1B3 are not induced by RIF, and further investigation of OATP1B induction by RIF and other nuclear receptor activators in humans is warranted. SIGNIFICANCE STATEMENT: In this study, combined endogenous biomarker and gene expression data suggested that RIF did not induce OATP1B in cynomolgus monkeys. For the first time, the study determines transporter gene expression in the nonhuman primate liver, gut, and kidney tissues after administration of RIF for 7 days, leading to a better understanding of the induction of OATP1B and other major drug transporters. Finally, it provides evidence to strengthen the claim that coproporphyrin is a suitable endogenous probe of OATP1B activity.

Hepatocyte Concentrations of Imaging Compounds Associated with Transporter Inhibition: Evidence in Perfused Rat Livers

In the liver, several approaches are used to investigate and predict the complex issue of drug-induced transporter inhibition. These approaches include in vitro assays and pharmacokinetic models that predict how inhibitors modify the systemic and liver concentrations of the victim drugs. Imaging is another approach that shows how inhibitors might alter liver concentrations stronger than systemic concentrations. In perfused rat livers associated with a gamma counter that measures liver concentrations continuously, we previously showed how fluxes across transporters generate the hepatocyte concentrations of two clinical imaging compounds, one with a low extraction ratio [gadobenate dimeglumine (BOPTA)] and one with a high extraction ratio [mebrofenin (MEB)]. BOPTA and MEB are transported by rat organic anion transporting polypeptide and multiple resistance-associated protein 2, which are both inhibited by rifampicin. The aim of the study is to measure how rifampicin modifies the hepatocyte concentrations and membrane clearances of BOPTA and MEB and to determine whether these compounds might be used to investigate transporter-mediated drug-drug interactions in clinical studies. We show that rifampicin coperfusion greatly decreases BOPTA hepatocyte concentrations, but increases those of MEB. Rifampicin strongly decreases BOPTA hepatic clearance. In contrast, rifampicin decreases moderately MEB hepatic clearance and blocks the biliary intrinsic clearance, increasing MEB hepatocyte concentrations. In conclusion, low concentrations prevent the quantification of BOPTA biliary intrinsic clearance, while MEB is a promising imaging probe substrate to evidence transporter-mediated drug-drug interactions when inhibitors act on influx and efflux transporters.

A Strategy to Refine the Phenotyping Approach and Its Implementation to Predict Drug Clearance: A Physiologically Based Pharmacokinetic Simulation Study

The phenotyping approach to predict drug metabolism activity is often hampered by a lack of correlation between the probe and the drug of interest. In this article, we present a strategy to refine the phenotyping approach based on a physiologically based pharmacokinetic simulation (implemented in Simcyp Simulator version 17) using previously published models. The apparent clearance (CL/F) of erlotinib was better predicted by the sum of caffeine and i.v. midazolam CL/F (r²  = 0.60) compared to that of either probe drug alone. The clearance of atorvastatin and repaglinide had a strong correlation (r²  = 0.70 and 0.63, respectively) with that of pitavastatin (a SLCO1B1 probe). Use of multiple probes for drugs that are predominantly metabolized by more than one cytochrome P450 (CYP) enzyme should be considered. In a case in which hepatic uptake transporters play a significant role in the disposition of a drug, the pharmacokinetic of a transporter probe will provide better predictions of the drug clearance.