Effect of Hepatic Organic Anion‐Transporting Polypeptide 1B Inhibition and Chronic Kidney Disease on the Pharmacokinetics of a Liver‐Targeted Glucokinase Activator: A Model‐Based Evaluation

Mechanistic Determinants of Daprodustat Drug-Drug Interactions and Pharmacokinetics in Hepatic Dysfunction and Chronic Kidney Disease: Significance of OATP1B-CYP2C8 Interplay

Daprodustat is the first oral hypoxia-inducible factor prolyl hydroxylase inhibitor approved recently for the treatment of anemia caused by chronic kidney disease (CKD) in adults receiving dialysis. We evaluated the role of organic anion transporting polypeptide (OATP)1B-mediated hepatic uptake transport in the pharmacokinetics (PKs) of daprodustat using in vitro and in vivo studies, and physiologically-based PK (PBPK) modeling of its drug-drug interactions (DDIs) with inhibitor drugs. In vitro, daprodustat showed specific transport by OATP1B1/1B3 in the transfected cell systems and primary human and monkey hepatocytes. A single-dose oral rifampin (OATP1B inhibitor) reduced daprodustat intravenous clearance by a notable 9.9 ± 1.2-fold (P < 0.05) in cynomolgus monkeys. Correspondingly, volume of distribution at steady-state was also reduced by 5.0 ± 1.1-fold, whereas the half-life change was minimal (1.5-fold), corroborating daprodustat hepatic uptake inhibition by rifampin. A PBPK model accounting for OATP1B-CYP2C8 interplay was developed, which well described daprodustat PK and DDIs with gemfibrozil (CYP2C8 and OATP1B inhibitor) and trimethoprim (weak CYP2C8 inhibitor) within 25% error of the observed data in healthy subjects. About 18-fold increase in daprodustat area under the curve (AUC) following gemfibrozil treatment was found to be associated with strong CYP2C8 inhibition and moderate OATP1B inhibition. Moreover, PK modulation in hepatic dysfunction and subjects with CKD, in comparison to healthy control, was well-captured by the model. CYP2C8 and/or OATP1B inhibitor drugs (e.g., gemfibrozil, clopidogrel, rifampin, and cyclosporine) were predicted to perpetrate moderate-to-strong DDIs in healthy subjects, as well as, in target CKD population. Daprodustat can be used as a sensitive CYP2C8 index substrate in the absence of OATP1B modulation.

Membrane transporters in drug development and as determinants of precision medicine

The effect of membrane transporters on drug disposition, efficacy and safety is now well recognized. Since the initial publication from the International Transporter Consortium, significant progress has been made in understanding the roles and functions of transporters, as well as in the development of tools and models to assess and predict transporter-mediated activity, toxicity and drug-drug interactions (DDIs). Notable advances include an increased understanding of the effects of intrinsic and extrinsic factors on transporter activity, the application of physiologically based pharmacokinetic modelling in predicting transporter-mediated drug disposition, the identification of endogenous biomarkers to assess transporter-mediated DDIs and the determination of the cryogenic electron microscopy structures of SLC and ABC transporters. This article provides an overview of these key developments, highlighting unanswered questions, regulatory considerations and future directions.

Chronic kidney disease and physiologically based pharmacokinetic modeling: a critical review of existing models

INTRODUCTION

Physiologically based pharmacokinetic (PBPK) modeling is a paradigm shift in this era for determining the exposure of drugs in pediatrics, geriatrics, and patients with chronic diseases where clinical trials are difficult to conduct.

AREAS COVERED

This review has collated data regarding published PBPK models on chronic kidney disease (CKD), including the drug and system-specific input model parameters and model evaluation criteria. Four databases were used from 13th June 2023 to 10th July 2023 for identifying the relevant studies that met the inclusion/exclusion criteria. Alterations in plasma protein (albumin/alpha-1 acid glycoprotein), gastric emptying time, hematocrit, small intestinal transit time, the abundance of cytochrome (CYP) 450 enzymes, glomerular filtration rate, and physicochemical parameters for different drugs were explicitly elaborated from earlier reported studies. Moreover, model evaluation depicted that models in CKD for most of the included drugs were within the allowed two-fold error range.

EXPERT OPINION

This review will provide insights for researchers on applying PBPK models in managing patients with different levels of CKD to prevent undesirable side effects and increase the effectiveness of drug therapy.

Understanding Statin-Roxadustat Drug-Drug-Disease Interaction Using Physiologically-Based Pharmacokinetic Modeling

A different drug-drug interaction (DDI) scenario may exist in patients with chronic kidney disease (CKD) compared with healthy volunteers (HVs), depending on the interplay between drug-drug and disease (drug-drug-disease interaction (DDDI)). Physiologically-based pharmacokinetic (PBPK) modeling, in lieu of a clinical trial, is a promising tool for evaluating these complex DDDIs in patients. However, the prediction confidence of PBPK modeling in the severe CKD population is still low when nonrenal pathways are involved. More mechanistic virtual disease population and robust validation cases are needed. To this end, we aimed to: (i) understand the implications of severe CKD on statins (atorvastatin, simvastatin, and rosuvastatin) pharmacokinetics (PK) and DDI; and (ii) predict untested clinical scenarios of statin-roxadustat DDI risks in patients to guide suitable dose regimens. A novel virtual severe CKD population was developed incorporating the disease effect on both renal and nonrenal pathways. Drug and disease PBPK models underwent a four-way validation. The verified PBPK models successfully predicted the altered PKs in patients for substrates and inhibitors and recovered the observed statin-rifampicin DDIs in patients and the statin-roxadustat DDIs in HVs within 1.25- and 2-fold error. Further sensitivity analysis revealed that the severe CKD effect on statins PK is mainly mediated by hepatic BCRP for rosuvastatin and OATP1B1/3 for atorvastatin. The magnitude of statin-roxadustat DDI in patients with severe CKD was predicted to be similar to that in HVs. PBPK-guided suitable dose regimens were identified to minimize the risk of side effects or therapeutic failure of statins when co-administered with roxadustat.

Evaluation of Renal Impairment Influence on Metabolic Drug Clearance using a Modelling Approach

BACKGROUND AND OBJECTIVE

Chronic kidney disease (CKD) may alter drug renal elimination but is also known for interacting with hepatic metabolism via multiple uremic components. However, few global models, considering the five major cytochromes, have been published, and none specifically address the decrease in cytochrome P450 (CYP450) activity. The aim of our study was to estimate the possibility of quantifying residual cytochrome activity as a function of filtration rate, according to the data available in the literature.

METHODS

For each drug in the DDI-predictor database, we collected available pharmacokinetic data comparing drug exposition in the healthy patient and in various stages of CKD, before building a model capable of predicting the variation of exposure according to the degree of renal damage. We followed an In vivo Mechanistic Static Model (IMSM) approach, previously validated for predicting change in liver clearance. We estimated the remaining fraction parameters at glomerular filtration rate (GFR) = 0 and the alpha value of GFR to 50% impairment for the 5 major cytochromes using a non-linear constrained regression using Matlab software.

RESULTS

Thirty-one compounds had usable pharmacokinetic data, with 51 AUC ratios between healthy and renal impaired patients. The remaining CYP3A4 activity was estimated to be 0.4 when CYP2D6, 2C9, 2C19 and 1A2 activity was estimated to be 0.43; 1; 0.73 and 0.7, respectively. The alpha value was estimated to be at 6.62; 25; 9.8; 1.38 and 11.04 for each cytochrome. In comparison with published data, all estimates but one were correctly predicted in the range of 0.5-2.

CONCLUSION

Our approach was able to describe the impact of CKD on metabolic elimination. Modelling this process makes it possible to anticipate changes in clearance and drug exposure in CKD patients, with the advantage of greater simplicity than approaches based on physiologically-based pharmacokinetic modelling. However, a precise estimation of the impact of renal failure is not possible with an IMSM approach due to the large variability of the published data, and thus should rely on specific pharmacokinetic modelling for narrow therapeutic margin drugs.

Effect of Hepatic Impairment on OATP1B Activity: Quantitative Pharmacokinetic Analysis of Endogenous Biomarker and Substrate Drugs

Hepatic impairment (HI) is known to modulate drug disposition and may lead to elevated plasma exposure. The aim of this study was to quantitate the in vivo OATP1B-mediated hepatic uptake activity in populations with varying degrees of HI. First, we measured baseline levels of plasma coproporphyrin-I, an endogenous OATP1B biomarker, in an open-label, parallel cohort study in adult subjects with normal liver function and mild, moderate, and severe HI (n = 24, 6/cohort). The geometric mean plasma concentrations of coproporphyrin-I were 1.66-fold, 2.81-fold (P < 0.05), and 7.78-fold (P < 0.0001) higher in mild, moderate, and severe impairment than those healthy controls. Second, we developed a dataset of 21 OATP1B substrate drugs with HI data extracted from literature. Median disease-to-healthy plasma area under the curve (AUC) ratios for substrate drugs were ~ 1.4, 3.0, and 6.4 for mild, moderate, and severe HI, respectively. Additionally, significant linear relationship was noted between AUC ratios of substrate drugs without and with co-administration of rifampin, a prototypic OATP1B inhibitor, and AUC ratios in moderate (P < 0.01) and severe (P < 0.001) HI. Third, a physiologically-based pharmacokinetic model analysis was conducted with 10 substrate drugs following estimation of relative OATP1B functional activity in virtual disease population models using coproporphyrin-I data and verification of substrate models with rifampin drug-drug interaction data. This approach adequately predicted plasma AUC change particularly in moderate (9 of 10 within 2-fold) and severe (5 of 5 within 2-fold) HI. Collective findings indicate progressive reduction, by as much as 90-92%, in OATP1B activity in the HI population.

Coproporphyrin I as an Endogenous Biomarker to Detect Reduced OATP1B Activity and Shift in Elimination Route in Chronic Kidney Disease

Coproporphyrin I (CPI) is an endogenous biomarker of organic anion transporting polypeptide 1B transporter (OATP1B). CPI plasma baseline was reported to increase with severity of chronic kidney disease (CKD). Further, ratio of CPI area under the plasma concentration-time curve (AUCR) in the presence/absence of OATP1B inhibitor rifampin was higher in patients with CKD compared with healthy participants, in contrast to pitavastatin (a clinical OATP1B probe). This study investigated mechanism(s) contributing to altered CPI baseline in patients with CKD by extending a previously developed physiologically-based pharmacokinetic (PBPK) model to this patient population. CKD-related covariates were evaluated in a stepwise manner on CPI fraction unbound in plasma (f_u,p ), OATP1B-mediated hepatic uptake clearance (CL_active ), renal clearance (CL_R ), and endogenous synthesis (k_syn ). The CPI model successfully recovered increased baseline and rifampin-mediated AUCR in patients with CKD by accounting for the following disease-related changes: 13% increase in f_u,p , 29% and 39% decrease in CL_active in mild and moderate to severe CKD, respectively, decrease in CL_R proportional to decline in glomerular filtration rate, and 27% decrease in k_syn in severe CKD. Almost complete decline in CPI renal elimination in severe CKD increased its fraction transported by OATP1B, rationalizing differences in the CPI-rifampin interaction observed between healthy participants and patients with CKD. In conclusion, mechanistic modeling performed here supports CKD-related decrease in OATP1B function to inform prospective PBPK modeling of OATP1B-mediated drug-drug interaction in these patients. Monitoring of CPI allows detection of CKD-drug interaction risk for OATP1B drugs with combined hepatic and renal elimination which may be underestimated by extrapolating the interaction risk based on pitavastatin data in healthy participants.

Applications, Challenges, and Outlook for PBPK Modeling and Simulation: A Regulatory, Industrial and Academic Perspective

Several regulatory guidances on the use of physiologically based pharmacokinetic (PBPK) analyses and physiologically based biopharmaceutics model(s) (PBBM(s)) have been issued. Workshops are routinely held, demonstrating substantial interest in applying these modeling approaches to address scientific questions in drug development. PBPK models and PBBMs have remarkably contributed to model-informed drug development (MIDD) such as anticipating clinical PK outcomes affected by extrinsic and intrinsic factors in general and specific populations. In this review, we proposed practical considerations for a "base" PBPK model construction and development, summarized current status, challenges including model validation and gaps in system models, and future perspectives in PBPK evaluation to assess a) drug metabolizing enzyme(s)- or drug transporter(s)- mediated drug-drug interactions b) dosing regimen prediction, sampling timepoint selection and dose validation in pediatric patients from newborns to adolescents, c) drug exposure in patients with renal and/or and hepatic organ impairment, d) maternal-fetal drug disposition during pregnancy, and e) pH-mediated drug-drug interactions in patients treated with proton pump inhibitors/acid-reducing agents (PPIs/ARAs) intended for gastric protection. Since PBPK can simulate outcomes in clinical studies with enrollment challenges or ethical issues, the impact of PBPK models on waivers and how to strengthen study waiver is discussed.

Clinical Implications of Altered Drug Transporter Abundance/Function and PBPK Modeling in Specific Populations: An ITC Perspective

The role of membrane transporters on pharmacokinetics (PKs), drug-drug interactions (DDIs), pharmacodynamics (PDs), and toxicity of drugs has been broadly recognized. However, our knowledge of modulation of transporter expression and/or function in the diseased patient population or specific populations, such as pediatrics or pregnancy, is still emerging. This white paper highlights recent advances in studying the changes in transporter expression and activity in various diseases (i.e., renal and hepatic impairment and cancer) and some specific populations (i.e., pediatrics and pregnancy) with the focus on clinical implications. Proposed alterations in transporter abundance and/or activity in diseased and specific populations are based on (i) quantitative transporter proteomic data and relative abundance in specific populations vs. healthy adults, (ii) clinical PKs, and emerging transporter biomarker and/or pharmacogenomic data, and (iii) physiologically-based pharmacokinetic modeling and simulation. The potential for altered PK, PD, and toxicity in these populations needs to be considered for drugs and their active metabolites in which transporter-mediated uptake/efflux is a major contributor to their absorption, distribution, and elimination pathways and/or associated DDI risk. In addition to best practices, this white paper discusses current challenges and knowledge gaps to study and quantitatively predict the effects of modulation in transporter activity in these populations, together with the perspectives from the International Transporter Consortium (ITC) on future directions.

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.

Transporter-mediated drug-drug interactions: advancement in models, analytical tools, and regulatory perspective

Drug-drug interactions mediated by transporters are a serious clinical concern hence a tremendous amount of work has been done on the characterization of the transporter-mediated proteins in humans and animals. The underlying mechanism for the transporter-mediated drug-drug interaction is the induction or inhibition of the transporter which is involved in the cellular uptake and efflux of drugs. Transporter of the brain, liver, kidney, and intestine are major determinants that alter the absorption, distribution, metabolism, excretion profile of drugs, and considerably influence the pharmacokinetic profile of drugs. As a consequence, transporter proteins may affect the therapeutic activity and safety of drugs. However, mounting evidence suggests that many drugs change the activity and/or expression of the transporter protein. Accordingly, evaluation of drug interaction during the drug development process is an integral part of risk assessment and regulatory requirements. Therefore, this review will highlight the clinical significance of the transporter, their role in disease, possible cause underlying the drug-drug interactions using analytical tools, and update on the regulatory requirement. The recent in-silico approaches which emphasize the advancement in the discovery of drug-drug interactions are also highlighted in this review. Besides, we discuss several endogenous biomarkers that have shown to act as substrates for many transporters, which could be potent determinants to find the drug-drug interactions mediated by transporters. Transporter-mediated drug-drug interactions are taken into consideration in the drug approval process therefore we also provided the extrapolated decision trees from in-vitro to in-vivo, which may trigger the follow-up to clinical studies.

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.

Predicting the Human Hepatic Clearance of Acidic and Zwitterionic Drugs

Prospective predictions of human hepatic clearance for anionic/zwitterionic compounds, which are oftentimes subjected to transporter-mediated uptake, are challenging in drug discovery. We evaluated the utility of preclinical species, rats and cynomolgus monkeys [nonhuman primates (NHPs)], to predict the human hepatic clearance using a diverse set of acidic/zwitterionic drugs. Preclinical clearance data were generated following intravenous dosing in rats/NHPs and compared to the human clearance data (n = 18/27). Single-species scaling of NHP clearance with an allometric exponent of 0.50 allowed for good prediction of human clearance (fold error ∼2.1, bias ∼1.0), with ∼86% predictions within 3-fold. In comparison, rats underpredicted the clearance of lipophilic acids, while overprediction was noted for hydrophilic acids. Finally, an in vitro clearance assay based on human hepatocytes, which is routinely used in discovery setting, markedly underpredicted human clearance (bias ∼0.12). Collectively, this study provides insights into the usefulness of the preclinical models in enabling pharmacokinetic optimization for acid/zwitterionic drug candidates.

Novel Mechanistic PBPK Model to Predict Renal Clearance in Varying Stages of CKD by Incorporating Tubular Adaptation and Dynamic Passive Reabsorption

Chronic kidney disease (CKD) has significant effects on renal clearance (CLr ) of drugs. Physiologically-based pharmacokinetic (PBPK) models have been used to predict CKD effects on transporter-mediated renal active secretion and CLr for hydrophilic nonpermeable compounds. However, no studies have shown systematic PBPK modeling of renal passive reabsorption or CLr for hydrophobic permeable drugs in CKD. The goal of this study was to expand our previously developed and verified mechanistic kidney model to develop a universal model to predict changes in CLr in CKD for permeable and nonpermeable drugs that accounts for the dramatic nonlinear effect of CKD on renal passive reabsorption of permeable drugs. The developed model incorporates physiologically-based tubular changes of reduced water reabsorption/increased tubular flow rate per remaining functional nephron in CKD. The final adaptive kidney model successfully (absolute fold error (AFE) all < 2) predicted renal passive reabsorption and CLr for 20 permeable and nonpermeable test compounds across the stages of CKD. In contrast, use of proportional glomerular filtration rate reduction approach without addressing tubular adaptation processes in CKD to predict CLr generated unacceptable CLr predictions (AFE = 2.61-7.35) for permeable compounds in severe CKD. Finally, the adaptive kidney model accurately predicted CLr of para-amino-hippuric acid and memantine, two secreted compounds, in CKD, suggesting successful integration of active secretion into the model, along with passive reabsorption. In conclusion, the developed adaptive kidney model enables mechanistic predictions of in vivo CLr through CKD progression without any empirical scaling factors and can be used for CLr predictions prior to assessment of drug disposition in renal impairment.

A Microdose Cocktail to Evaluate Drug Interactions in Patients with Renal Impairment

Renal impairment (RI) is known to influence the pharmacokinetics of nonrenally eliminated drugs, although the mechanism and clinical impact is poorly understood. We assessed the impact of RI and single dose oral rifampin (RIF) on the pharmacokinetics of CYP3A, OATP1B, P-gp, and BCRP substrates using a microdose cocktail and OATP1B endogenous biomarkers. RI alone had no impact on midazolam (MDZ), maximum plasma concentration (C_max ), and area under the curve (AUC), but a progressive increase in AUC with RI severity for dabigatran (DABI), and up to ~2-fold higher AUC for pitavastatin (PTV), rosuvastatin (RSV), and atorvastatin (ATV) for all degrees of RI was observed. RIF did not impact MDZ, had a progressively smaller DABI drug-drug interaction (DDI) with increasing RI severity, a similar 3.1-fold to 4.4-fold increase in PTV and RSV AUC in healthy volunteers and patients with RI, and a diminishing DDI with RI severity from 6.1-fold to 4.7-fold for ATV. Endogenous biomarkers of OATP1B (bilirubin, coproporphyrin I/III, and sulfated bile salts) were generally not impacted by RI, and RIF effects on these biomarkers in RI were comparable or larger than those in healthy volunteers. The lack of a trend with RI severity of PTV and several OATP1B biomarkers, suggests that mechanisms beyond RI directly impacting OATP1B activity could also be considered. The DABI, RSV, and ATV data suggest an impact of RI on intestinal P-gp, and potentially BCRP activity. Therefore, DDI data from healthy volunteers may represent a worst-case scenario for clinically derisking P-gp and BCRP substrates in the setting of RI.