Mechanistic modeling to predict the transporter- and enzyme-mediated drug-drug interactions of repaglinide

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.

Interplay of UDP-Glucuronosyltransferase and CYP2C8 for CYP2C8 Mediated Drug Oxidation and Its Impact on Drug-Drug Interaction Produced by Standardized CYP2C8 Inhibitors, Clopidogrel and Gemfibrozil

BACKGROUND AND OBJECTIVE

Early investigations into drug-drug interactions (DDIs) involving cytochrome P450 2C8 (CYP2C8) have highlighted the complexity of interactions between CYP2C8 substrate drugs, including montelukast, desloratadine, pioglitazone, repaglinide, and cerivastatin (the latter two being OATP1B1 substrates), and standardized CYP2C8 inhibitors such as clopidogrel (Clop) and gemfibrozil (Gem). These interactions have proven challenging to predict based solely on simple CYP inhibition. A hypothesis has emerged suggesting that these substrate drugs first distribute to UDP-glucuronosyltransferase (UGT) before undergoing oxidation by CYP2C8, resulting in bidirectional elimination. The process of drug distribution to UGT is believed to significantly impact these DDIs. This study aims to explore the intricate interplay between UGT and CYP2C8 in the context of DDIs involving CYP2C8 substrates affected by Clop and Gem.

METHODS

Plasma-level data for the unchanged drug and its metabolite, drawn from the respective literature, formed the basis of our analysis. We evaluated the enzymatic inhibitory activities of DDIs and utilized simulations to estimate plasma levels of the unchanged victim drug and its metabolite in each DDI. This was accomplished by employing a functional relationship that considered the fractional contributions of CYP2C8 and UGT to clearance, perpetrator-specific inhibitory activities against CYP2C8, and drug distribution to UGT.

RESULTS

Our findings emphasize the pivotal role of UGT-mediated distribution in the context of CYP2C8 substrate metabolism, particularly in the complex DDIs induced by Clop and Gem. In these DDIs, Gem exerts inhibitory effects on both UGT and CYP2C8, whereas Clop (specifically its metabolite, Clop-COOH) solely targets CYP2C8. Importantly, the inhibition of CYP2C8 by both Clop and Gem is achieved through a non-competitive mechanism, driven by the actions of their acyl-glucuronides. Clop and Gem exhibit inhibition activities accounting for 85% (pAi,CYP2C8 = 7) and 93% (pAi,CYP2C8 = 15), respectively. In contrast, Gem's inhibition of UGT is relatively modest (50%, pAi,UGT(d) = 2), and it operates through a non-specific, competitive process in drug distribution to UGT. Within this context, our UGT-CYP2C8 interplay model offers an accurate means of predicting the alterations resulting from DDIs, encompassing changes in plasma levels of the unchanged drug and its metabolites, as well as shifts in metabolite formation rates. Our analysis highlights the critical importance of considering the fractional contributions of CYP2C8 and UGT to the victim drug's clearance (fm,CYP2C8; fm,UGT) in DDI prediction. Furthermore, our examination of DDIs involving OATP1B1 substrate drugs underscores that accounting for the hepatic uptake transporters' role in the liver is superfluous in DDI prediction.

CONCLUSION

These findings substantially enhance our comprehension of CYP2C8-mediated oxidation and DDIs, holding crucial implications for drug development and the planning of clinical trials involving these inhibitors.

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.

Studying the right transporter at the right time: an in vitro strategy for assessing drug-drug interaction risk during drug discovery and development

INTRODUCTION

Transporters are significant in dictating drug pharmacokinetics, thus inhibition of transporter function can alter drug concentrations resulting in drug-drug interactions (DDIs). Because they can impact drug toxicity, transporter DDIs are a regulatory concern for which prediction of clinical effect from in vitro data is critical to understanding risk.

AREA COVERED

The authors propose in vitro strategies to assist mitigating/removing transporter DDI risk during development by frontloading specific studies, or managing patient risk in the clinic. An overview of clinically relevant drug transporters and observed DDIs are provided, alongside presentation of key considerations/recommendations for in vitro study design evaluating drugs as inhibitors or substrates. Guidance on identifying critical co-medications, clinically relevant disposition pathways and using mechanistic static equations for quantitative prediction of DDI is compiled.

EXPERT OPINION

The strategies provided will facilitate project teams to study the right transporter at the right time to minimise development risks associated with DDIs. To truly alleviate or manage clinical risk, the industry will benefit from moving away from current qualitative basic static equation approaches to transporter DDI hazard assessment towards adopting the use of mechanistic models to enable quantitative DDI prediction, thereby contextualising risk to ascertain whether a transporter DDI is simply pharmacokinetic or clinically significant requiring intervention.

Non-uniformity of Changes in Drug-Metabolizing Enzymes and Transporters in Liver Cirrhosis: Implications for Drug Dosage Adjustment

Liver cirrhosis is a chronic disease that affects the liver structure, protein expression, and overall metabolic function. Abundance data for drug-metabolizing enzymes and transporters (DMET) across all stages of disease severity are scarce. Levels of these proteins are crucial for the accurate prediction of drug clearance in hepatically impaired patients using physiologically based pharmacokinetic (PBPK) models, which can be used to guide the selection of more precise dosing. This study aimed to experimentally quantify these proteins in human liver samples and assess how they can impact the predictive performance of the PBPK models. We determined the absolute abundance of 51 DMET proteins in human liver microsomes across the three degrees of cirrhosis severity (n = 32; 6 mild, 13 moderate, and 13 severe), compared to histologically normal controls (n = 14), using QconCAT-based targeted proteomics. The results revealed a significant but non-uniform reduction in the abundance of enzymes and transporters, from control, by 30–50% in mild, 40–70% in moderate, and 50–90% in severe cirrhosis groups. Cancer and/or non-alcoholic fatty liver disease-related cirrhosis showed larger deterioration in levels of CYP3A4, 2C8, 2E1, 1A6, UGT2B4/7, CES1, FMO3/5, EPHX1, MGST1/3, BSEP, and OATP2B1 than the cholestasis set. Drug-specific pathways together with non-uniform changes of abundance across the enzymes and transporters under various degrees of cirrhosis necessitate the use of PBPK models. As case examples, such models for repaglinide, dabigatran, and zidovudine were successful in recovering disease-related alterations in drug exposure. In conclusion, the current study provides the biological rationale behind the absence of a single dose adjustment formula for all drugs in cirrhosis and demonstrates the utility of proteomics-informed PBPK modeling for drug-specific dose adjustment in liver cirrhosis.

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.

Physiologically Based Pharmacokinetic Modelling to Identify Pharmacokinetic Parameters Driving Drug Exposure Changes in the Elderly

BACKGROUND

Medication use is highly prevalent with advanced age, but clinical studies are rarely conducted in the elderly, leading to limited knowledge regarding age-related pharmacokinetic changes.

OBJECTIVE

The objective of this study was to investigate which pharmacokinetic parameters determine drug exposure changes in the elderly by conducting virtual clinical trials for ten drugs (midazolam, metoprolol, lisinopril, amlodipine, rivaroxaban, repaglinide, atorvastatin, rosuvastatin, clarithromycin and rifampicin) using our physiologically based pharmacokinetic (PBPK) framework.

METHODS

PBPK models for all ten drugs were developed in young adults (20-50 years) following the best practice approach, before predicting pharmacokinetics in the elderly (≥ 65 years) without any modification of drug parameters. A descriptive relationship between age and each investigated pharmacokinetic parameter (peak concentration [C_max], time to C_max [t_max], area under the curve [AUC], clearance, volume of distribution, elimination-half-life) was derived using the final PBPK models, and verified with independent clinically observed data from 52 drugs.

RESULTS

The age-related changes in drug exposure were successfully simulated for all ten drugs. Pharmacokinetic parameters were predicted within 1.25-fold (70%), 1.5-fold (86%) and 2-fold (100%) of clinical data. AUC increased progressively by 0.9% per year throughout adulthood from the age of 20 years, which was explained by decreased clearance, while C_max, t_max and volume of distribution were not affected by human aging. Additional clinical data of 52 drugs were contained within the estimated variability of the established age-dependent correlations for each pharmacokinetic parameter.

CONCLUSION

The progressive decrease in hepatic and renal blood flow, as well as glomerular filtration, rate led to a reduced clearance driving exposure changes in the healthy elderly, independent of the drug.

Impact of gastrointestinal tract variability on oral drug absorption and pharmacokinetics: An UNGAP review

The absorption of oral drugs is frequently plagued by significant variability with potentially serious therapeutic consequences. The source of variability can be traced back to interindividual variability in physiology, differences in special populations (age- and disease-dependent), drug and formulation properties, or food-drug interactions. Clinical evidence for the impact of some of these factors on drug pharmacokinetic variability is mounting: e.g. gastric pH and emptying time, small intestinal fluid properties, differences in pediatrics and the elderly, and surgical changes in gastrointestinal anatomy. However, the link of colonic factors variability (transit time, fluid composition, microbiome), sex differences (male vs. female) and gut-related diseases (chronic constipation, anorexia and cachexia) to drug absorption variability has not been firmly established yet. At the same time, a way to decrease oral drug pharmacokinetic variability is provided by the pharmaceutical industry: clinical evidence suggests that formulation approaches employed during drug development can decrease the variability in oral exposure. This review outlines the main drivers of oral drug exposure variability and potential approaches to overcome them, while highlighting existing knowledge gaps and guiding future studies in this area.

Quantitative analysis of an impact of P-glycoprotein on edoxaban's disposition using a human physiologically based pharmacokinetic (PBPK) model

The purpose of this study was to evaluate the impact of P-glycoprotein (P-gp) efflux on edoxaban absorption in gastrointestinal tracts quantitatively by a physiologically based pharmacokinetic (PBPK) model constructed with clinical and non-clinical observations (using GastroPlus™ software). An absorption process was described by the advanced compartmental absorption and transit model with the P-gp function. A human PBPK model was constructed by integrating the clinical and non-clinical observations. The constructed model was demonstrated to reproduce the data observed in the mass-balance study. Thus, elimination pathways can be quantitatively incorporated into the model. A constructed model successfully described the difference in slopes of plasma concentration (Cp)-time curve at around 8 - 24 hr post-dose between intravenous infusion and oral administration. Furthermore, the model without P-gp efflux activity can reproduce the Cp-time profile in the absence of P-gp activity observed from the clinical DDI study results. Since the difference of slopes between intravenous infusion and oral administration also disappeared by the absence of P-gp efflux activity, P-gp must be a key molecule to govern edoxaban's PK behavior. The constructed PBPK model will help us to understand the significant contribution of P-gp in edoxaban's disposition in gastrointestinal tracts quantitatively.

Hepatic transporter-mediated pharmacokinetic drug-drug interactions: Recent studies and regulatory recommendations

Transporter-mediated drug-drug interactions are one of the major mechanisms in pharmacokinetic-based drug interactions and correspondingly affecting drugs' safety and efficacy. Regulatory bodies underlined the importance of the evaluation of transporter-mediated interactions as a part of the drug development process. The liver is responsible for the elimination of a wide range of endogenous and exogenous compounds via metabolism and biliary excretion. Therefore, hepatic uptake transporters, expressed on the sinusoidal membranes of hepatocytes, and efflux transporters mediating the transport from hepatocytes to the bile are determinant factors for pharmacokinetics of drugs, and hence, drug-drug interactions. In parallel with the growing research interest in this area, regulatory guidances have been updated with detailed assay models and criteria. According to well-established preclinical results, observed or expected hepatic transporter-mediated drug-drug interactions can be taken into account for clinical studies. In this paper, various methods including in vitro, in situ, in vivo, in silico approaches, and combinational concepts and several clinical studies on the assessment of transporter-mediated drug-drug interactions were reviewed. Informative and effective evaluation by preclinical tools together with the integration of pharmacokinetic modeling and simulation can reduce unexpected clinical outcomes and enhance the success rate in drug development.

Prediction of Cyclosporin-Mediated Drug Interaction Using Physiologically Based Pharmacokinetic Model Characterizing Interplay of Drug Transporters and Enzymes

Uptake transporter organic anion transporting polypeptides (OATPs), efflux transporters (P-gp, BCRP and MRP2) and cytochrome P450 enzymes (CYP450s) are widely expressed in the liver, intestine or kidney. They coordinately work to control drug disposition, termed as "interplay of transporters and enzymes". Cyclosporine A (CsA) is an inhibitor of OATPs, P-gp, MRP2, BCRP and CYP3As. Drug-drug interaction (DDI) of CsA with victim drugs occurs via disordering interplay of transporters and enzymes. We aimed to establish a whole-body physiologically-based pharmacokinetic (PBPK) model which predicts disposition of CsA and nine victim drugs including atorvastatin, cerivastatin, pravastatin, rosuvastatin, fluvastatin, simvastatin, lovastatin, repaglinide and bosentan, as well as drug-drug interactions (DDIs) of CsA with nine victim drugs to investigate the integrated effect of enzymes and transporters in liver, intestinal and kidney on drug disposition. Predictions were compared with observations. Most of the predictions were within 0.5-2.0 folds of observations. Atorvastatin was represented to investigate individual contributions of transporters and CYP3As to atorvastatin disposition and their integrated effect. The contributions to atorvastatin disposition were hepatic OATPs >> hepatic CYP3A > intestinal CYP3As ≈ efflux transporters (P-gp/BCRP/MRP2). The results got the conclusion that the developed PBPK model characterizing the interplay of enzymes and transporters was successfully applied to predict the pharmacokinetics of 10 OATP substrates and DDIs of CsA with 9 victim drugs.

Clinical Data Combined With Modeling and Simulation Indicate Unchanged Drug-Drug Interaction Magnitudes in the Elderly

Age-related comorbidities and consequently polypharmacy are highly prevalent in the elderly, resulting in an increased risk for drug-drug interactions (DDIs). The effect of aging on DDI magnitudes is mostly uncertain, leading to missing guidance regarding the clinical DDI management in the elderly. Clinical data obtained in aging people living with HIV ≥ 55 years, who participated in the Swiss HIV Cohort Study, demonstrated unchanged DDI magnitudes with advanced aging for four studied DDI scenarios. These data plus published data for midazolam in the presence of clarithromycin and rifampicin in elderly individuals assessed the predictive potential of the used physiologically-based pharmacokinetic (PBPK) model to simulate DDIs in the elderly. All clinically observed data were generally predicted within the 95% confidence interval of the PBPK simulations. The verified model predicted subsequently the magnitude of 50 DDIs across adulthood (20-99 years) with 42 scenarios being only verified in adults aged 20-50 years in the absence of clinically observed data in the elderly. DDI magnitudes were not impacted by aging regardless of the involved drugs, DDI mechanism, mediators of DDIs, or the sex of the investigated individuals. The prediction of unchanged DDI magnitudes with advanced aging were proofed by 17 published, independent DDIs that were investigated in young and elderly subjects. In conclusion, this study demonstrated by combining clinically observed data with modeling and simulation that aging does not impact DDI magnitudes and thus, clinical management of DDIs can a priori be similar in aging men and women in the absence of severe comorbidities.

Physiologically based pharmacokinetic modeling to assess metabolic drug-drug interaction risks and inform the drug label for fedratinib

Purpose

Fedratinib (INREBIC^®), a Janus kinase 2 inhibitor, is approved in the United States to treat patients with myelofibrosis. Fedratinib is not only a substrate of cytochrome P450 (CYP) enzymes, but also exhibits complex auto-inhibition, time-dependent inhibition, or mixed inhibition/induction of CYP enzymes including CYP3A. Therefore, a mechanistic modeling approach was used to characterize pharmacokinetic (PK) properties and assess drug–drug interaction (DDI) potentials for fedratinib under clinical scenarios.

Methods

The physiologically based pharmacokinetic (PBPK) model of fedratinib was constructed in Simcyp^® (V17R1) by integrating available in vitro and in vivo information and was further parameterized and validated by using clinical PK data.

Results

The validated PBPK model was applied to predict DDIs between fedratinib and CYP modulators or substrates. The model simulations indicated that the fedratinib-as-victim DDI extent in terms of geometric mean area under curve (AUC) at steady state is about twofold or 1.2-fold when strong or moderate CYP3A4 inhibitors, respectively, are co-administered with repeated doses of fedratinib. In addition, the PBPK model successfully captured the perpetrator DDI effect of fedratinib on a sensitive CY3A4 substrate midazolam and predicted minor effects of fedratinib on CYP2C8/9 substrates.

Conclusions

The PBPK-DDI model of fedratinib facilitated drug development by identifying DDI potential, optimizing clinical study designs, supporting waivers for clinical studies, and informing drug label claims. Fedratinib dose should be reduced to 200 mg QD when a strong CYP3A4 inhibitor is co-administered and then re-escalated to 400 mg in a stepwise manner as tolerated after the strong CYP3A4 inhibitor is discontinued.

Electronic supplementary material

The online version of this article (10.1007/s00280-020-04131-y) contains supplementary material, which is available to authorized users.

Physiologically-Based Pharmacokinetic Models for Evaluating Membrane Transporter Mediated Drug-Drug Interactions: Current Capabilities, Case Studies, Future Opportunities, and Recommendations

Physiologically-based pharmacokinetic (PBPK) modeling has been extensively used to quantitatively translate in vitro data and evaluate temporal effects from drug-drug interactions (DDIs), arising due to reversible enzyme and transporter inhibition, irreversible time-dependent inhibition, enzyme induction, and/or suppression. PBPK modeling has now gained reasonable acceptance with the regulatory authorities for the cytochrome-P450-mediated DDIs and is routinely used. However, the application of PBPK for transporter-mediated DDIs (tDDI) in drug development is relatively uncommon. Because the predictive performance of PBPK models for tDDI is not well established, here, we represent and discuss examples of PBPK analyses included in regulatory submission (the US Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the Pharmaceuticals and Medical Devices Agency (PMDA)) across various tDDIs. The goal of this collaborative effort (involving scientists representing 17 pharmaceutical companies in the Consortium and from academia) is to reflect on the use of current databases and models to address tDDIs. This challenges the common perceptions on applications of PBPK for tDDIs and further delves into the requirements to improve such PBPK predictions. This review provides a reflection on the current trends in PBPK modeling for tDDIs and provides a framework to promote continuous use, verification, and improvement in industrialization of the transporter PBPK modeling.

Perspectives from the Innovation and Quality Consortium Induction Working Group on Factors Impacting Clinical Drug-Drug Interactions Resulting from Induction: Focus on Cytochrome 3A Substrates

A recent publication from the Innovation and Quality Consortium Induction Working Group collated a large clinical data set with the goal of evaluating the accuracy of drug-drug interaction (DDI) prediction from in vitro data. Somewhat surprisingly, comparison across studies of the mean- or median-reported area under the curve ratio showed appreciable variability in the magnitude of outcome. This commentary explores the possible drivers of this range of outcomes observed in clinical induction studies. While recommendations on clinical study design are not being proposed, some key observations were informative during the aggregate analysis of clinical data. Although DDI data are often presented using median data, individual data would enable evaluation of how differences in study design, baseline expression, and the number of subjects contribute. Since variability in perpetrator pharmacokinetics (PK) could impact the overall DDI interpretation, should this be routinely captured? Maximal induction was typically observed after 5-7 days of dosing. Thus, when the half-life of the inducer is less than 30 hours, are there benefits to a more standardized study design? A large proportion of CYP3A4 inducers were also CYP3A4 inhibitors and/or inactivators based on in vitro data. In these cases, using CYP3A selective substrates has limitations. More intensive monitoring of changes in area under the curve over time is warranted. With selective CYP3A substrates, the net effect was often inhibition, whereas less selective substrates could discern induction through mechanisms not susceptible to inhibition. The latter included oral contraceptives, which raise concerns of reduced efficacy following induction. Alternative approaches for modeling induction, such as applying biomarkers and physiologically based pharmacokinetic modeling (PBPK), are also considered. SIGNIFICANCE STATEMENT: The goal of this commentary is to stimulate discussion on whether there are opportunities to optimize clinical drug-drug interaction study design. The overall aim is to reduce, understand and contextualize the variability observed in the magnitude of induction across reported clinical studies. A large clinical CYP3A induction dataset was collected and further analyzed to identify trends and gaps. Reporting individual victim PK data, characterizing perpetrator PK and including additional PK assessments for mixed-mechanism perpetrators may provide insights into how these factors impact differences observed in clinical outcomes. The potential utility of biomarkers and PBPK modeling are discussed in considering future directions.

Impact of Intracellular Concentrations on Metabolic Drug-Drug Interaction Studies

Accurate prediction of drug-drug interactions (DDI) is a challenging task in drug discovery and development. It requires determination of enzyme inhibition in vitro which is highly system-dependent for many compounds. The aim of this study was to investigate whether the determination of intracellular unbound concentrations in primary human hepatocytes can be used to bridge discrepancies between results obtained using human liver microsomes and hepatocytes. Specifically, we investigated if Kp_uu could reconcile differences in CYP enzyme inhibition values (K_i or IC₅₀). Firstly, our methodology for determination of Kp_uu was optimized for human hepatocytes, using four well-studied reference compounds. Secondly, the methodology was applied to a series of structurally related CYP2C9 inhibitors from a Roche discovery project. Lastly, the Kp_uu values of three commonly used CYP3A4 inhibitors—ketoconazole, itraconazole, and posaconazole—were determined and compared to compound-specific hepatic enrichment factors obtained from physiologically based modeling of clinical DDI studies with these three compounds. Kp_uu obtained in suspended human hepatocytes gave good predictions of system-dependent differences in vitro. The Kp_uu was also in fair agreement with the compound-specific hepatic enrichment factors in DDI models and can therefore be used to improve estimations of enrichment factors in physiologically based pharmacokinetic modeling.

CYP450s-Activity Relations of Celastrol to Interact with Triptolide Reveal the Reasons of Hepatotoxicity of Tripterygium wilfordii

Celastrol and triptolide, as the two main bio-activity ingredients in Tripterygium wilfordii, have wide anticancer pharmacological potency, as well as anti-inflammatory and immunosuppression effects. However, they have potential hepatotoxicity and underlying mechanisms of them-induced toxicity mediated by hepatic CYP450s have not been well delineated. In the present study, we accessed the toxic effects and possible mechanism of celastrol and triptolide on primary rat hepatocytes. Models of subdued/enhanced activity of CYP450 enzymes in primary rat hepatocytes were also constructed to evaluate the relationship between the two ingredients and CYP450s. LC-MS/MS was used to establish a detection method of the amount of triptolide in rat hepatocytes. As the results, cell viability, biochemical index, and mitochondrial membrane potential indicated that celastrol and triptolide had toxic potencies on hepatocytes. Moreover, the toxic effects were enhanced when the compounds combined with 1-aminobenzotriazole (enzyme inhibitor) while they were mitigated when combined with phenobarbital (an enzyme inducer). Meanwhile, celastrol could affect the amount of triptolide in the cell. We therefore put forward that increase of triptolide in the cell might be one of the main causes of hepatotoxicity caused by Tripterygium wilfordii.

Use of Physiologically Based Pharmacokinetic Modeling to Evaluate the Effect of Chronic Kidney Disease on the Disposition of Hepatic CYP2C8 and OATP1B Drug Substrates

Chronic kidney disease (CKD) differentially affects the pharmacokinetics (PK) of nonrenally cleared drugs via certain pathways (e.g., cytochrome P450 (CYP)2D6); however, the effect on CYP2C8‐mediated clearance is not well understood because of overlapping substrate specificity with hepatic organic anion‐transporting polypeptides (OATPs). This study used physiologically based pharmacokinetic (PBPK) modeling to delineate potential changes in CYP2C8 or OATP1B activity in patients with CKD. Drugs analyzed are predominantly substrates of CYP2C8 (rosiglitazone and pioglitazone), OATP1B (pitavastatin), or both (repaglinide). Following initial model verification, pharmacokinetics (PK) of these drugs were simulated in patients with severe CKD considering changes in glomerular filtration rate (GFR), plasma protein binding, and activity of either CYP2C8 and/or OATP1B in a stepwise manner. The PBPK analysis suggests that OATP1B activity could be decreased up to 60% in severe CKD, whereas changes to CYP2C8 are negligible. This improved understanding of CKD effect on clearance pathways could be important to inform the optimal use of nonrenally eliminated drugs in patients with CKD.

Comparison of uptake transporter functions in hepatocytes in different species to determine the optimal model for evaluating drug transporter activities in humans

A thorough understanding of species-dependent differences in hepatic uptake transporters is critical for predicting human pharmacokinetics (PKs) from preclinical data. In this study, the activities of organic anion transporting polypeptide (OATP/Oatp), organic cation transporter 1 (OCT1/Oct1), and sodium-taurocholate cotransporting polypeptide (NTCP/Ntcp) in cultured rat, dog, monkey and human hepatocytes were compared. The activities of hepatic uptake transporters were evaluated with respect to culture duration, substrate and species-dependent differences in hepatocytes. Longer culture duration reduced hepatic uptake transporter activities across species except for Oatp and Ntcp in rats. Comparable apparent Michaelis-Menten constant (K_m,app) values in hepatocytes were observed across species for atorvastatin, estradiol-17β-glucuronide and metformin. The K_m,app values for rosuvastatin and taurocholate were significantly different across species. Rat hepatocytes exhibited the highest Oatp percentage of uptake transporter-mediated permeation clearance (PS_inf,act) while no difference in %PS_inf,act of probe substrates were observed across species. The in vitro hepatocyte inhibition data in rats, monkeys and humans provided reasonable predictions of in vivo drug-drug interaction (DDIs) between atorvastatin/rosuvastatin and rifampin. These findings suggested that using human hepatocytes with a short culture time is the most robust preclinical model for predicting DDIs for compounds exhibiting active hepatic uptake in humans.

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

For dual transporter-enzyme substrate drugs, the extended clearance model can be used to predict the rate-determining step(s) (RDS) of a drug and hence predict its drug-drug interaction (DDI) liabilities (i.e., transport, metabolism, or both). If the RDS of the hepatic clearance of the drug is sinusoidal uptake clearance (CL^s _in), even if the drug is eliminated mainly by hepatic metabolism, its DDI liability (as viewed from changes to systemic drug concentrations) is expected to be inhibition or induction of uptake transporters but not hepatic enzymes; however, this is true only if the condition required to maintain CL^s _in as the RDS is maintained. Here, we illustrate through theoretical simulations that the RDS condition may be violated in the presence of a DDI. That is, the RDS of a drug can switch from CL^s _in to all hepatobiliary clearances [i.e., metabolic/biliary clearance (CL_{met + bile}) and CL^s _in], leading to unexpected systemic DDIs, such as metabolic DDIs, when only transporter DDIs were anticipated. As expected, these analyses revealed that the RDS switch depends on the ratio of CL_{met + bile} to sinusoidal efflux clearance (CL^s _ef). Additional analyses revealed that for intravenously administered drugs, the RDS switch also depends on the magnitude of CL^s _in We analyzed published in vitro quantified hepatobiliary clearances and observed that most drugs have a CL_{met + bile}/CL^s _ef ratio < 4; hence, in practice, the magnitude of CL^s _in must be considered when establishing the RDS. These analyses provide insights previously not appreciated and a theoretical framework to predict DDI liabilities for drugs that are dual transporter-enzyme substrates.

Clopidogrel Pharmacokinetics in Malaysian Population Groups: The Impact of Inter-Ethnic Variability

Malaysia is a multi-ethnic society whereby the impact of pharmacogenetic differences between ethnic groups may contribute significantly to variability in clinical therapy. One of the leading causes of mortality in Malaysia is cardiovascular disease (CVD), which accounts for up to 26% of all hospital deaths annually. Clopidogrel is used as an adjunct treatment in the secondary prevention of cardiovascular events. CYP2C19 plays an integral part in the metabolism of clopidogrel to the active metabolite clopi-H4. However, CYP2C19 genetic polymorphism, prominent in Malaysians, could influence target clopi-H4 plasma concentrations for clinical efficacy. This study addresses how inter-ethnicity variability within the Malaysian population impacts the attainment of clopi-H4 target plasma concentration under different CYP2C19 polymorphisms through pharmacokinetic (PK) modelling. We illustrated a statistically significant difference (P < 0.001) in the clopi-H4 C_max between the extensive metabolisers (EM) and poor metabolisers (PM) phenotypes with either Malay or Malaysian Chinese population groups. Furthermore, the number of PM individuals with peak clopi-H4 concentrations below the minimum therapeutic level was partially recovered using a high-dose strategy (600 mg loading dose followed by a 150 mg maintenance dose), which resulted in an approximate 50% increase in subjects attaining the minimum clopi-H4 plasma concentration for a therapeutic effect.

Guiding dose adjustment of amlodipine after co-administration with ritonavir containing regimens using a physiologically-based pharmacokinetic/pharmacodynamic model

Amlodipine, a commonly prescribed anti-hypertensive drug, shows increased systemic exposure with cytochrome P450 (CYP) 3A inhibitors. Ritonavir (RTV) is a potent mechanism-based and reversible CYP3A inhibitor and moderate inducer that is used as a pharmacokinetic enhancer in several antiviral treatment regimens. Drug-drug interaction (DDI) between RTV and amlodipine is due to mixed inhibition and induction of CYP3A4, which is challenging to predict without a mechanistic model that accounts for the complexity of both mechanisms occurring simultaneously. A novel physiologically-based pharmacokinetic (PBPK) model was developed for amlodipine, and the model was verified using published clinical PK and DDI data. The verified amlodipine PBPK model was linked to a pharmacodynamics model that describes changes in systolic blood pressure (SBP) during and after co-administration with RTV. The magnitude and time course of RTV effects on amlodipine plasma exposures and SBP were evaluated, to provide guidance on dose adjustment of amlodipine during and after co-administration with RTV-containing regimens. Model simulations suggested that the increase in amlodipine's plasma exposure by RTV diminishes by approximately 80% within 5 days after the last dose of RTV. PBPK simulations suggested that resuming a full dose of amlodipine [5 mg once daily (QD)] immediately after RTV's last dose would decrease daily average SBP by a maximum of 3.3 mmHg, while continuing with the reduced dose (2.5 mg QD) for 5 days after the last dose of RTV would increase daily average SBP by a maximum of 5.8 mmHg. Based on these results, either approach of resuming amlodipine's full dose could be appropriate when combined with appropriate clinical monitoring.

Implications of intercorrelation between hepatic CYP3A4-CYP2C8 enzymes for the evaluation of drug-drug interactions: a case study with repaglinide

AIMS

Statistically significant positive correlations are reported for the abundance of hepatic drug-metabolizing enzymes. We investigate, as an example, the impact of CYP3A4-CYP2C8 intercorrelation on the predicted interindividual variabilities of clearance and drug-drug interactions (DDIs) for repaglinide using physiologically based pharmacokinetic (PBPK) modelling.

METHODS

PBPK modelling and simulation were employed using Simcyp Simulator (v15.1). Virtual populations were generated assuming intercorrelations between hepatic CYP3A4-CYP2C8 abundances derived from observed values in 24 human livers. A repaglinide PBPK model was used to predict PK parameters in the presence and absence of gemfibrozil in virtual populations, and the results were compared with a clinical DDI study.

RESULTS

Coefficient of variation (CV) of oral clearance was 52.5% in the absence of intercorrelation between CYP3A4-CYP2C8 abundances, which increased to 54.2% when incorporating intercorrelation. In contrast, CV for predicted DDI (as measured by AUC ratio before and after inhibition) was reduced from 46.0% in the absence of intercorrelation between enzymes to 43.8% when incorporating intercorrelation: these CVs were associated with 5th/95th percentiles (2.48-11.29 vs. 2.49-9.69). The range of predicted DDI was larger in the absence of intercorrelation (1.55-77.06) than when incorporating intercorrelation (1.79-25.15), which was closer to clinical observations (2.6-12).

CONCLUSIONS

The present study demonstrates via a systematic investigation that population-based PBPK modelling incorporating intercorrelation led to more consistent estimation of extreme values than those observed in interindividual variabilities of clearance and DDI. As the intercorrelations more realistically reflect enzyme abundances, virtual population studies involving PBPK and DDI should avoid using Monte Carlo assignment of enzyme abundance.

Sandwich-Cultured Hepatocytes for Mechanistic Understanding of Hepatic Disposition of Parent Drugs and Metabolites by Transporter-Enzyme Interplay

Functional interplay between transporters and drug-metabolizing enzymes is currently one of the hottest topics in the field of drug metabolism and pharmacokinetics. Uptake transporter-enzyme interplay is important to determine intrinsic hepatic clearance based on the extended clearance concept. Enzyme and efflux transporter interplay, which includes both sinusoidal (basolateral) and canalicular efflux transporters, determines the fate of metabolites formed in the liver. As sandwich-cultured hepatocytes (SCHs) maintain metabolic activities and form a canalicular network, the whole interplay between uptake and efflux transporters and drug-metabolizing enzymes can be investigated simultaneously. In this article, we review the utility and applicability of SCHs for mechanistic understanding of hepatic disposition of both parent drugs and metabolites. In addition, the utility of SCHs for mimicking species-specific disposition of parent drugs and metabolites in vivo is described. We also review application of SCHs for clinically relevant prediction of drug-drug interactions caused by drugs and metabolites. The usefulness of mathematical modeling of hepatic disposition of parent drugs and metabolites in SCHs is described to allow a quantitative understanding of an event in vitro and to develop a more advanced model to predict in vivo disposition.

Prediction of Drug-Drug Interactions with Bupropion and Its Metabolites as CYP2D6 Inhibitors Using a Physiologically-Based Pharmacokinetic Model

The potential of inhibitory metabolites of perpetrator drugs to contribute to drug-drug interactions (DDIs) is uncommon and underestimated. However, the occurrence of unexpected DDI suggests the potential contribution of metabolites to the observed DDI. The aim of this study was to develop a physiologically-based pharmacokinetic (PBPK) model for bupropion and its three primary metabolites—hydroxybupropion, threohydrobupropion and erythrohydrobupropion—based on a mixed “bottom-up” and “top-down” approach and to contribute to the understanding of the involvement and impact of inhibitory metabolites for DDIs observed in the clinic. PK profiles from clinical researches of different dosages were used to verify the bupropion model. Reasonable PK profiles of bupropion and its metabolites were captured in the PBPK model. Confidence in the DDI prediction involving bupropion and co-administered CYP2D6 substrates could be maximized. The predicted maximum concentration (C_max) area under the concentration-time curve (AUC) values and C_max and AUC ratios were consistent with clinically observed data. The addition of the inhibitory metabolites into the PBPK model resulted in a more accurate prediction of DDIs (AUC and C_max ratio) than that which only considered parent drug (bupropion) P450 inhibition. The simulation suggests that bupropion and its metabolites contribute to the DDI between bupropion and CYP2D6 substrates. The inhibitory potency from strong to weak is hydroxybupropion, threohydrobupropion, erythrohydrobupropion, and bupropion, respectively. The present bupropion PBPK model can be useful for predicting inhibition from bupropion in other clinical studies. This study highlights the need for caution and dosage adjustment when combining bupropion with medications metabolized by CYP2D6. It also demonstrates the feasibility of applying the PBPK approach to predict the DDI potential of drugs undergoing complex metabolism, especially in the DDI involving inhibitory metabolites.

Physiologically Based Pharmacokinetic Modeling to Predict Drug-Drug Interactions with Efavirenz Involving Simultaneous Inducing and Inhibitory Effects on Cytochromes

BACKGROUND

Antiretroviral drugs are among the therapeutic agents with the highest potential for drug-drug interactions (DDIs). In the absence of clinical data, DDIs are mainly predicted based on preclinical data and knowledge of the disposition of individual drugs. Predictions can be challenging, especially when antiretroviral drugs induce and inhibit multiple cytochrome P450 (CYP) isoenzymes simultaneously.

METHODS

This study predicted the magnitude of the DDI between efavirenz, an inducer of CYP3A4 and inhibitor of CYP2C8, and dual CYP3A4/CYP2C8 substrates (repaglinide, montelukast, pioglitazone, paclitaxel) using a physiologically based pharmacokinetic (PBPK) modeling approach integrating concurrent effects on CYPs. In vitro data describing the physicochemical properties, absorption, distribution, metabolism, and elimination of efavirenz and CYP3A4/CYP2C8 substrates as well as the CYP-inducing and -inhibitory potential of efavirenz were obtained from published literature. The data were integrated in a PBPK model developed using mathematical descriptions of molecular, physiological, and anatomical processes defining pharmacokinetics. Plasma drug-concentration profiles were simulated at steady state in virtual individuals for each drug given alone or in combination with efavirenz. The simulated pharmacokinetic parameters of drugs given alone were compared against existing clinical data. The effect of efavirenz on CYP was compared with published DDI data.

RESULTS

The predictions indicate that the overall effect of efavirenz on dual CYP3A4/CYP2C8 substrates is induction of metabolism. The magnitude of induction tends to be less pronounced for dual CYP3A4/CYP2C8 substrates with predominant CYP2C8 metabolism.

CONCLUSION

PBPK modeling constitutes a useful mechanistic approach for the quantitative prediction of DDI involving simultaneous inducing or inhibitory effects on multiple CYPs as often encountered with antiretroviral drugs.

The Application of Physiologically Based Pharmacokinetic Modeling to Predict the Role of Drug Transporters: Scientific and Regulatory Perspectives

Transporters play an important role in drug absorption, disposition, and drug action. The evaluation of drug transporters requires a comprehensive understanding of transporter biology and pharmacology. Physiologically based pharmacokinetic (PBPK) models may offer an integrative platform to quantitatively evaluate the role of drug transporters and its interplay with other drug disposition processes such as passive drug diffusion and elimination by metabolizing enzymes. To date, PBPK modeling and simulations integrating drug transporters lag behind that for drug-metabolizing enzymes. In addition, predictive performance of PBPK has not been well established for predicting the role of drug transporters in the pharmacokinetics of a drug. To enhance overall predictive performance of transporter-based PBPK models, it is necessary to have a detailed understanding of transporter biology for proper representation in the models and to have a quantitative understanding of the contribution of transporters in the absorption and metabolism of a drug. This article summarizes PBPK-based submissions evaluating the role of drug transporters to the Office of Clinical Pharmacology of the US Food and Drug Administration.

Physiologically Based Pharmacokinetic Modeling Suggests Limited Drug-Drug Interaction Between Clopidogrel and Dasabuvir

Dasabuvir, a nonnucleoside NS5B polymerase inhibitor, is a sensitive substrate of cytochrome P450 (CYP) 2C8 with a potential for drug–drug interaction (DDI) with clopidogrel. A physiologically based pharmacokinetic (PBPK) model was developed for dasabuvir to evaluate the DDI potential with clopidogrel, the acyl‐β‐D glucuronide metabolite of which has been reported as a strong mechanism‐based inhibitor of CYP2C8 based on an interaction with repaglinide. In addition, the PBPK model for clopidogrel and its metabolite were updated with additional in vitro data. Sensitivity analyses using these PBPK models suggested that CYP2C8 inhibition by clopidogrel acyl‐β‐D glucuronide may not be as potent as previously suggested. The dasabuvir and updated clopidogrel PBPK models predict a moderate increase of 1.5–1.9‐fold for C_max and 1.9–2.8‐fold for AUC of dasabuvir when coadministered with clopidogrel. While the PBPK results suggest there is a potential for DDI between dasabuvir and clopidogrel, the magnitude is not expected to be clinically relevant.

Utilizing PBPK Modeling to Evaluate the Potential of a Significant Drug-Drug Interaction Between Clopidogrel and Dasabuvir: A Scientific Perspective

Dasabuvir, a component of VIEKIRA PAK, is a substrate of CYP2C8 enzymes. Prescribing information for VIEKIRA PAK contraindicates gemfibrozil, a strong CYP2C8 inhibitor, because coadministration significantly increases dasabuvir exposures, which may increase the risk of QT prolongation. Clopidogrel may increase dasabuvir exposures primarily due to CYP2C8 inhibition by clopidogrel-acyl-β-D-glucuronide. This commentary outlines the US Food and Drug Administration (FDA) interdisciplinary review team's scientific perspective to address the potential for a significant drug-drug interaction (DDI) between clopidogrel and VIEKIRA PAK.

Sandwich-Cultured Hepatocytes as a Tool to Study Drug Disposition and Drug-Induced Liver Injury

Sandwich-cultured hepatocytes (SCH) are metabolically competent and have proper localization of basolateral and canalicular transporters with functional bile networks. Therefore, this cellular model is a unique tool that can be used to estimate biliary excretion of compounds. SCH have been used widely to assess hepatobiliary disposition of endogenous and exogenous compounds and metabolites. Mechanistic modeling based on SCH data enables estimation of metabolic and transporter-mediated clearances, which can be used to construct physiologically based pharmacokinetic models for prediction of drug disposition and drug-drug interactions in humans. In addition to pharmacokinetic studies, SCH also have been used to study cytotoxicity and perturbation of biological processes by drugs and hepatically generated metabolites. Human SCH can provide mechanistic insights underlying clinical drug-induced liver injury (DILI). In addition, data generated in SCH can be integrated into systems pharmacology models to predict potential DILI in humans. In this review, applications of SCH in studying hepatobiliary drug disposition and bile acid-mediated DILI are discussed. An example is presented to show how data generated in the SCH model were used to establish a quantitative relationship between intracellular bile acids and cytotoxicity, and how this information was incorporated into a systems pharmacology model for DILI prediction.

Transporter-Mediated Hepatic Uptake Plays an Important Role in the Pharmacokinetics and Drug-Drug Interactions of Montelukast

Montelukast, a leukotriene receptor antagonist commonly prescribed for treatment of asthma, is primarily metabolized by cytochrome P450 (CYP)2C8, and has been suggested as a probe substrate for investigating CYP2C8 activity in vivo. We evaluated the quantitative role of hepatic uptake transport in its pharmacokinetics and drug-drug interactions (DDIs). Montelukast was characterized with significant active uptake in human hepatocytes, and showed affinity towards organic anion transporting polypeptides (OATPs) in transfected cell systems. Single-dose rifampicin, an OATP inhibitor, decreased montelukast clearance in rats and monkeys. Clinical DDIs of montelukast were evaluated using physiologically based pharmacokinetic modeling; and simulation of the interactions with gemfibrozil-CYP2C8 and OATP1B1/1B3 inhibitor, clarithromycin-CYP3A and OATP1B1/1B3 inhibitor, and itraconazole-CYP3A inhibitor, implicated OATPs-CYP2C8-CYP2C8 interplay as the primary determinant of montelukast pharmacokinetics. In conclusion, hepatic uptake plays a key role in the pharmacokinetics of montelukast, which should be taken into account when interpreting clinical interactions.

Comparison of the static in vivo approach to a physiologically based pharmacokinetic approach for metabolic drug–drug interactions prediction

Background: The in vivo mechanistic static model (IMSM) and the physiologically based pharmacokinetic (PBPK) model are two approaches used to predict the magnitude of drug–drug interactions (DDIs). The aim of this study was to evaluate the performance of IMSM and to compare IMSM with the PBPK approach implemented in Simcyp. Methods: The predictive performances of IMSM were evaluated on a panel of 628 DDIs. Subsequently, the IMSM and PBPK approaches were compared on a set of 104 DDIs. Results: The IMSM yielded 85% of predictions within 1.5-fold of the observed value on the 628 DDIs panel. The predictive performances of IMSM were better than those of the PBPK approach (median fold error 1 vs 0.86 on 104 studies; p = 0.02). Conclusion: The IMSM approach is an alternative tool for metabolic DDIs prediction.

Role of gemfibrozil as an inhibitor of CYP2C8 and membrane transporters

INTRODUCTION

Cytochrome P450 (CYP) 2C8 is a drug metabolizing enzyme of major importance. The lipid-lowering drug gemfibrozil has been identified as a strong inhibitor of CYP2C8 in vivo. This effect is due to mechanism-based inhibition of CYP2C8 by gemfibrozil 1-O-β-glucuronide. In vivo, gemfibrozil is a fairly selective CYP2C8 inhibitor, which lacks significant inhibitory effect on other CYP enzymes. Gemfibrozil can, however, have a smaller but clinically meaningful inhibitory effect on membrane transporters, such as organic anion transporting polypeptide 1B1 and organic anion transporter 3. Areas covered: This review describes the inhibitory effects of gemfibrozil on CYP enzymes and membrane transporters. The clinical drug interactions caused by gemfibrozil and the different mechanisms contributing to the interactions are reviewed in detail. Expert opinion: Gemfibrozil is a useful probe inhibitor of CYP2C8 in vivo, but its effect on membrane transporters has to be taken into account in study design and interpretation. Moreover, gemfibrozil could be used to boost the pharmacokinetics of CYP2C8 substrate drugs. Identification of gemfibrozil 1-O-β-glucuronide as a potent mechanism-based inhibitor of CYP2C8 has led to recognition of glucuronide metabolites as perpetrators of drug-drug interactions. Recently, also acyl glucuronide metabolites of clopidogrel and deleobuvir have been shown to strongly inhibit CYP2C8.

The regulation of human hepatic drug transporter expression by activation of xenobiotic-sensing nuclear receptors

INTRODUCTION

If a drug is found to be an inducer of hepatic drug metabolizing enzymes via activation of nuclear receptors such as pregnane X receptor (PXR) or constitutive androstane receptor (CAR), it is likely that drug transporters regulated through these same receptors will be induced as well. This review highlights what is currently known about the molecular mechanisms that regulate transporter expression and where the research is directed. Areas covered: This review is focused on publications that describe the role of activated hepatic nuclear receptors in the subsequent regulation of drug uptake and/or efflux transporters following exposure to xenobiotics. Expert opinion: Many of the published studies on the role of nuclear receptors in the regulation of drug transporters involve non-human test animals. But due to species response differences, these associations are not always applicable to humans. For this reason, some relevant human in vitro models have been developed, such as primary or cryopreserved human hepatocytes, human liver slices, or HepG2 or HuH7 cell lines transiently or stably transfected with PXR expression and reporter constructs as well as in vivo models such as PXR-humanized mice. These human-relevant test systems will continue to be developed and applied for the testing of investigational drugs.