Analysis of the repaglinide concentration increase produced by gemfibrozil and itraconazole based on the inhibition of the hepatic uptake transporter and metabolic enzymes

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.

Predicting transporter mediated drug-drug interactions via static and dynamic physiologically based pharmacokinetic modeling: A comprehensive insight on where we are now and the way forward

The greater utilization and acceptance of physiologically-based pharmacokinetic (PBPK) modeling to evaluate the potential metabolic drug-drug interactions is evident by the plethora of literature, guidance's, and regulatory dossiers available in the literature. In contrast, it is not widely used to predict transporter-mediated DDI (tDDI). This is attributed to the unavailability of accurate transporter tissue expression levels, the absence of accurate in vitro to in vivo extrapolations (IVIVE), enzyme-transporter interplay, and a lack of specific probe substrates. Additionally, poor understanding of the inhibition/induction mechanisms coupled with the inability to determine unbound concentrations at the interaction site made tDDI assessment challenging. Despite these challenges, continuous improvements in IVIVE approaches enabled accurate tDDI predictions. Furthermore, the necessity of extrapolating tDDI's to special (pediatrics, pregnant, geriatrics) and diseased (renal, hepatic impaired) populations is gaining impetus and is encouraged by regulatory authorities. This review aims to visit the current state-of-the-art and summarizes contemporary knowledge on tDDI predictions. The current understanding and ability of static and dynamic PBPK models to predict tDDI are portrayed in detail. Peer-reviewed transporter abundance data in special and diseased populations from recent publications were compiled, enabling direct input into modeling tools for accurate tDDI predictions. A compilation of regulatory guidance's for tDDI's assessment and success stories from regulatory submissions are presented. Future perspectives and challenges of predicting tDDI in terms of in vitro system considerations, endogenous biomarkers, the use of empirical scaling factors, enzyme-transporter interplay, and acceptance criteria for model validation to meet the regulatory expectations were discussed.

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.

Classification of drugs for evaluating drug interaction in drug development and clinical management

During new drug development, clinical drug interaction studies are carried out in accordance with the mechanism of potential drug interactions evaluated by in vitro studies. The obtained information should be provided efficiently to medical experts through package inserts and various information materials after the drug's launch. A recently updated Japanese guideline presents general procedures that are considered scientifically valid at the present moment. In this review, we aim to highlight the viewpoints of the Japanese guideline and enumerate drugs that were involved or are anticipated to be involved in evident pharmacokinetic drug interactions and classify them by their clearance pathway and potential intensity based on systematic reviews of the literature. The classification would be informative for designing clinical studies during the development stage, and the appropriate management of drug interactions in clinical practice.

PBPK Modeling Strategy for Predicting Complex Drug Interactions of Letermovir as a Perpetrator in Support of Product Labeling

Letermovir is a human cytomegalovirus (CMV) terminase inhibitor for the prophylaxis of CMV infection in allogeneic hematopoietic stem-cell transplant (HSCT) recipients. In vitro, letermovir is a time-dependent inhibitor and an inducer of cytochrome P450 (CYP)3A, and an inhibitor of CYP2C8 and organic anion transporting polypeptide (OATP)1B. A stepwise approach was taken to qualify the interaction model of an existing letermovir physiologically based pharmacokinetic model to predict letermovir interactions with CYP3A and OATP1B. The model was then used to prospectively predict the interaction between letermovir and CYP2C8 substrates such as repaglinide, a substrate of CYP2C8, CYP3A, and OATP1B. The results showed that letermovir modestly increased the exposure of CYP2C8 substrates. These results were used to inform the US prescribing information in the absence of clinical drug-drug interaction studies. In addition, midazolam interactions with letermovir at therapeutic doses were also simulated to confirm that letermovir is a moderate CYP3A inhibitor.

In Silico Prediction of Major Clearance Pathways of Drugs among 9 Routes with Two-Step Support Vector Machines

PURPOSE

The clearance pathways of drugs are critical elements for understanding the pharmacokinetics of drugs. We previously developed in silico systems to predict the five clearance pathway using a rectangular method and a support vector machine (SVM). In this study, we improved our classification system by increasing the number of clearance pathways available for our prediction (CYP1A2, CYP2C8, CYP2C19, and UDP-glucuronosyl transferases (UGTs)) and by accepting multiple major pathways.

METHODS

Using the four default descriptors (charge, molecular weight, logD at pH 7.0, and unbound fraction in plasma), three kinds of SVM-based predictors based on traditional single-step approach or two-step focusing approaches with subset or partition clustering were developed. The two-step approach with subset clustering resulted in the highest prediction performance. The feature-selection of additional descriptors based on a greedy algorithm was employed to further improve the predictability.

RESULTS

The prediction accuracy for each pathway was increased to more than 0.83 with the exception of CYP2C19 and UGTs pathways, whose accuracies were below 0.7. Prediction performance of CYP1A2, CYP3A4 and renal excretion pathways were found to be acceptable using external dataset.

CONCLUSIONS

We successfully constructed a novel SVM-based predictor for the multiple major clearance pathways based on chemical structures.

Quantitative Analysis of Complex Drug-Drug Interactions between Cerivastatin and Metabolism/Transport Inhibitors Using Physiologically Based Pharmacokinetic Modeling

Cerivastatin (CER) was withdrawn from the world market because of lethal rhabdomyolysis. Coadministrations of CER and cyclosporine A (CsA) or gemfibrozil (GEM) have been reported to increase the CER blood concentration. CsA is an inhibitor of organic anion transporting polypeptide (OATP)1B1 and CYP3A4, and GEM and its glucuronide (GEM-glu) inhibit OATP1B1 and CYP2C8. The purpose of this study was to describe the transporter-/enzyme-mediated drug-drug interactions (DDIs) of CER with CsA or GEM based on unified physiologically based pharmacokinetic (PBPK) models and to investigate whether the DDIs can be quantitatively analyzed by a bottom-up approach. Initially, the PBPK models for CER and GEM/GEM-glu were constructed based on the previously reported standard protocols. Next, the drug-dependent parameters were optimized by Cluster Newton Method. Thus, described concentration-time profiles for CER and GEM/GEM-glu agreed well with the clinically observed data. The DDIs were then simulated using the established PBPK models with previously obtained in vitro inhibition constants of CsA or GEM/GEM-glu against the OATP1B1 and cytochrome P450s. DDIs with the inhibitors were underestimated compared with observed data using the geometric means of reported values. To search for better described parameters within the range of in vitro values, sensitivity analyses were performed for DDIs of CER. Using the in vitro parameter sets selected by sensitivity analyses, these DDIs were well reproduced, indicating that the present PBPK models were able to describe adequately the clinical DDIs based on a bottom-up approach. The approaches in this study would be applicable to the prediction of other DDIs involving both transporters and metabolic enzymes.

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.

Drug interactions of meglitinide antidiabetics involving CYP enzymes and OATP1B1 transporter

Meglitinides such as repaglinide and nateglinide are useful to treat type 2 diabetes patients who follow a flexible lifestyle. They are short-acting insulin secretagogues and are associated with less risk of hypoglycemia, weight gain and chronic hyperinsulinemia compared with sulfonylureas. Meglitinides are the substrates of cytochrome P450 (CYP) enzymes and organic anion transporting polypeptide 1B1 (OATP1B1 transporter) and the coadministration of the drugs affecting them will result in pharmacokinetic drug interactions. This article focuses on the drug interactions of meglitinides involving CYP enzymes and OATP1B1 transporter. To prevent the risk of hypoglycemic episodes, prescribers and pharmacists must be aware of the adverse drug interactions of meglitinides.

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.

Analysis of the Change in the Blood Concentration-Time Profile Caused by Complex Drug-Drug Interactions in the Liver Considering the Enterohepatic Circulation: Examining Whether the Inhibition Constants for Uptake, Metabolism, and Biliary Excretion Can be Recovered by the Analyses Using Physiologically Based Pharmacokinetic Modeling

Hypothetical substrates undergoing transporter-mediated hepatic uptake, metabolism, and enterohepatic circulation with different rate-determining processes with a combination of inhibition constants (K_i) for hepatic uptake, metabolism, and biliary excretion processes were generated with a constant K_i for uptake and incorporated into a physiologically based pharmacokinetic model. Analyses of the kinetic model suggested that the fraction of substrates excreted in the bile to the total elimination by the liver (f_bile) can be estimated under certain conditions from kinetic analyses of their blood concentration-time profiles. Using the generated time profiles of substrates with and without coadministration of inhibitors, various pharmacokinetic parameters involving f_bile and K_i for the hepatic uptake, metabolism, and biliary excretion of drugs were back-calculated by fitting. Comparing parameters obtained with the original parameter sets by fitting, the K_i were found to be well estimated under the following conditions: the initial estimates for inhibition constants were relatively good, which corresponds to the case for obtaining reliable in vitro inhibition constants. In conclusion, the integration of top-down analyses with bottom-up estimates (experimental determination) of inhibition constants can be used to estimate in vivo inhibition constants and f_bile reliably.

Investigating Transporter-Mediated Drug-Drug Interactions Using a Physiologically Based Pharmacokinetic Model of Rosuvastatin

Rosuvastatin is a frequently used probe in transporter-mediated drug-drug interaction (DDI) studies. This report describes the development of a physiologically based pharmacokinetic (PBPK) model of rosuvastatin for prediction of pharmacokinetic (PK) DDIs. The rosuvastatin model predicted the observed single (i.v. and oral) and multiple dose PK profiles, as well as the impact of coadministration with transporter inhibitors. The predicted effects of rifampin and cyclosporine (6.58-fold and 5.07-fold increase in rosuvastatin area under the curve (AUC), respectively) were mediated primarily via inhibition of hepatic organic anion-transporting polypeptide (OATP)1B1 (Inhibition constant (K_i ) ∼1.1 and 0.014 µM, respectively) and OATP1B3 (K_i ∼0.3 and 0.007 µM, respectively), with cyclosporine also inhibiting intestinal breast cancer resistance protein (BCRP; K_i ∼0.07 µM). The predicted effects of gemfibrozil and its metabolite were moderate (1.88-fold increase in rosuvastatin AUC) and mediated primarily via inhibition of hepatic OATP1B1 and renal organic cation transporter 3. This model of rosuvastatin will be useful in prospectively predicting transporter-mediated DDIs with novel pharmaceutical agents in development.

Preincubation-dependent and long-lasting inhibition of organic anion transporting polypeptide (OATP) and its impact on drug-drug interactions

Preincubation with cyclosporin A (CsA), a potent inhibitor of organic anion transporting polypeptide 1B1 (OATP1B1) and OATP1B3, enhanced its inhibitory effects on these transporters in vitro. A similar effect was observed upon preincubation with some other inhibitors. Removing these from the incubation media did not readily reverse the inhibition on OATP1B1 and OATP1B3. This preincubation-dependent long-lasting inhibition appeared to be related to CsA concentration in the cells in addition to that in the incubation media. Thus, we hypothesized that CsA inhibits OATP1B1 and OATP1B3 from inside (trans-inhibition) as well as outside (cis-inhibition) the cells and constructed the cis- and trans-inhibition model. The enhanced inhibitory effect of CsA on OATP1B1 observed after preincubation was quantitatively described using K_i,out and K_i,in as inhibition constants for cis- and trans-inhibitions, respectively. In addition, a long-lasting inhibition was also described by this model. Additional factors taken into consideration when simulating in vivo pharmacokinetic alterations by CsA are potential inhibition by AM1, a major metabolite of CsA, which has been reported to inhibit OATP1B1 and OATP1B3. Based on the physiologically based pharmacokinetic model incorporating trans- and cis-inhibition of OATP1B1 by CsA, the simulation showed that OATP1B1-mediated drug-drug interaction with CsA was suggested to be time-dependent also in vivo although further clinical studies are required for confirmation.

Estimation of the Contribution of CYP2C8 and CYP3A4 in Repaglinide Metabolism by Human Liver Microsomes Under Various Buffer Conditions

We have previously reported that the microsomal activities of CYP2C8 and CYP3A4 largely depend on the buffer condition used in in vitro metabolic studies, with different patterns observed between the 2 isozymes. In the present study, therefore, the possibility of buffer condition dependence of the fraction metabolized by CYP2C8 (fm2C8) for repaglinide, a dual substrate of CYP2C8 and CYP3A4, was estimated using human liver microsomes under various buffer conditions. Montelukast and ketoconazole showed a potent and concentration-dependent inhibition of CYP2C8-mediated paclitaxel 6α-hydroxylation and CYP3A4-mediated triazolam α-hydroxylation, respectively, without dependence on the buffer condition. Repaglinide depletion was inhibited by both inhibitors, but the degree of inhibition depended on buffer conditions. Based on these results, the contribution of CYP2C8 in repaglinide metabolism was estimated to be larger than that of CYP3A4 under each buffer condition, and the fm2C8 value of 0.760, estimated in 50 mM phosphate buffer, was the closest to the value (0.801) estimated in our previous modeling analysis based on its concentration increase in a clinical drug interaction study. Researchers should be aware of the possibility of buffer condition affecting the estimated contribution of enzyme(s) in drug metabolism processes involving multiple enzymes.

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.

Clarification of the Mechanism of Clopidogrel-Mediated Drug-Drug Interaction in a Clinical Cassette Small-dose Study and Its Prediction Based on In Vitro Information

Clopidogrel is reported to be associated with cerivastatin-induced rhabdomyolysis, and clopidogrel and its metabolites are capable of inhibiting CYP2C8 and OATP 1B1 in vitro. The objective of the present study was to identify the mechanism of clopidogrel-mediated drug-drug interactions (DDIs) on the pharmacokinetics of OATP1B1 and/or CYP2C8 substrates in vivo. A clinical cassette small-dose study using OATPs, CYP2C8, and OATP1B1/CYP2C8 probe drugs (pitavastatin, pioglitazone, and repaglinide, respectively) with or without the coadministration of either 600 mg rifampicin (an inhibitor for OATPs), 200 mg trimethoprim (an inhibitor for CYP2C8), or 300 mg clopidogrel was performed, and the area under the concentration-time curve (AUC) ratios (AUCRs) for probe substrates were predicted using a static model. Clopidogrel increased the AUC of pioglitazone (2.0-fold) and repaglinide (3.1-fold) but did not significantly change the AUC of pitavastatin (1.1-fold). In addition, the AUC of pioglitazone M4, a CYP2C8-mediated metabolite of pioglitazone, was reduced to 70% of the control by coadministration of clopidogrel. The predicted AUCRs using the mechanism-based inhibition of CYP2C8 by clopidogrel acyl-β-glucuronide were similar to the observed AUCRs, and the predicted AUCR (1.1) of repaglinide using only the inhibition of OATP1B1 did not reach the observed AUCR (3.1). In conclusion, a single 300 mg of clopidogrel mainly inhibits CYP2C8-mediated metabolism by clopidogrel acyl-β-glucuronide, but its effect on the pharmacokinetics of OATP1B1 substrates is negligible. Clopidogrel is expected to have an effect not only on CYP2C8 substrates, but also dual CYP2C8/OATP1B1 substrates as seen in the case of repaglinide.

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms "PBPK" and "physiologically based pharmacokinetic model" to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.

Investigation of the impact of substrate selection on in vitro organic anion transporting polypeptide 1B1 inhibition profiles for the prediction of drug-drug interactions

The risk assessment of organic anion transporting polypeptide (OATP) 1B1-mediated drug-drug interactions (DDIs) is an indispensable part of drug development. We previously reported that in vitro inhibitory potencies of several inhibitors on OATP1B1 depend on the substrates when prototypical substrates, estradiol-17β-glucuronide (E₂G), estrone-3-sulfate, and sulfobromophthalein were used as test substrates. The purpose of this study was to comprehensively investigate this substrate-dependent inhibition of OATP1B1 using clinically relevant OATP1B1 inhibitors and substrate drugs. Effects of cyclosporine A (CsA), rifampin, and gemfibrozil on OATP1B1-mediated uptake of 12 substrate drugs were examined in OATP1B1-expressing human embryonic kidney 293 cells. The Ki values (μM) for CsA varied from 0.0771 to 0.486 (6.3-fold), for rifampin from 0.358 to 1.23 (3.4-fold), and for gemfibrozil from 9.65 to 252 (26-fold). Except for the inhibition of torasemide uptake by CsA and that of nateglinide uptake by gemfibrozil, the Ki values were within 2.8-fold of those obtained using E₂G as a substrate. Preincubation potentiated the inhibitory effect of CsA on OATP1B1 with similar magnitude regardless of the substrates. R values calculated based on a static model showed some variation depending on the Ki values determined with various substrates, and such variability could have an impact on the DDI predictions particularly for a weak-to-moderate inhibitor (gemfibrozil). OATP1B1 substrate drugs except for torasemide and nateglinide, or E₂G as a surrogate, is recommended as an in vitro probe in the inhibition experiments, which will help mitigate the risk of false-negative DDI predictions potentially caused by substrate-dependent Ki variation.

Reduced physiologically-based pharmacokinetic model of repaglinide: impact of OATP1B1 and CYP2C8 genotype and source of in vitro data on the prediction of drug-drug interaction risk

PURPOSE

To investigate the effect of OATP1B1 genotype as a covariate on repaglinide pharmacokinetics and drug-drug interaction (DDIs) risk using a reduced physiologically-based pharmacokinetic (PBPK) model.

METHODS

Twenty nine mean plasma concentration-time profiles for SLCO1B1 c.521T>C were used to estimate hepatic uptake clearance (CLuptake) in different genotype groups applying a population approach in NONMEM v.7.2.

RESULTS

Estimated repaglinide CLuptake corresponded to 217 and 113 μL/min/10(6) cells for SLCO1B1 c.521TT/TC and CC, respectively. A significant effect of OATP1B1 genotype was seen on CLuptake (48% reduction for CC relative to wild type). Sensitivity analysis highlighted the impact of CLmet and CLdiff uncertainty on the CLuptake optimization using plasma data. Propagation of this uncertainty had a marginal effect on the prediction of repaglinide OATP1B1-mediated DDI with cyclosporine; however, sensitivity of the predicted magnitude of repaglinide metabolic DDI was high. In addition, the reduced PBPK model was used to assess the effect of both CYP2C8*3 and SLCO1B1 c.521T>C on repaglinide exposure by simulations; power calculations were performed to guide prospective DDI and pharmacogenetic studies.

CONCLUSIONS

The application of reduced PBPK model for parameter optimization and limitations of this process associated with the use of plasma rather than tissue profiles are illustrated.

Substrate-dependent inhibition of organic anion transporting polypeptide 1B1: comparative analysis with prototypical probe substrates estradiol-17β-glucuronide, estrone-3-sulfate, and sulfobromophthalein

Organic anion transporting polypeptide (OATP) 1B1 plays an important role in the hepatic uptake of many drugs, and the evaluation of OATP1B1-mediated drug-drug interactions (DDIs) is emphasized in the latest DDI (draft) guidance documents from U.S. and E.U. regulatory agencies. It has been suggested that some OATP1B1 inhibitors show a discrepancy in their inhibitory potential, depending on the substrates used in the cell-based assay. In this study, inhibitory effects of 14 compounds on the OATP1B1-mediated uptake of the prototypical substrates [³H]estradiol-17β-glucuronide (E₂G), [³H]estrone-3-sulfate (E₁S), and [³H]sulfobromophthalein (BSP) were studied in OATP1B1-transfected cells. Inhibitory potencies of tested compounds varied depending on the substrates. Ritonavir, gemfibrozil, and erythromycin caused remarkable substrate-dependent inhibition with up to 117-, 14-, and 13-fold difference in their IC₅₀ values, respectively. Also, the clinically relevant OATP inhibitors rifampin and cyclosporin A exhibited up to 12- and 6-fold variation in their IC₅₀ values, respectively. Regardless of the inhibitors tested, the most potent OATP1B1 inhibition was observed when [³H]E₂G was used as a substrate. Mutual inhibition studies of OATP1B1 indicated that E₂G and E₁S competitively inhibited each other, whereas BSP noncompetitively inhibited E₂G uptake. In addition, BSP inhibited E₁S in a competitive manner, but E₁S caused an atypical kinetics on BSP uptake. This study showed substrate-dependent inhibition of OATP1B1 and demonstrated that E₂G was the most sensitive in vitro OATP1B1 probe substrate among three substrates tested. This will give us an insight into the assessment of clinically relevant OATP1B1-mediated DDI in vitro with minimum potential of false-negative prediction.

Drug metabolites as cytochrome p450 inhibitors: a retrospective analysis and proposed algorithm for evaluation of the pharmacokinetic interaction potential of metabolites in drug discovery and development

Understanding drug-drug interactions (DDIs) is a key component of clinical practice ensuring patient safety and efficacy of medicines. The role of drug metabolites in DDIs is a developing area of science, and has been recently highlighted in a draft regulatory guidance. The guidance states that metabolites representing ≥25% of the parent drug's area under the plasma concentration/time curve and/or >10% of exposure of total drug-related material should trigger in vitro characterization of metabolites for cytochrome P450 inhibition and propensity for DDIs. The relationship between in vitro cytochrome P450 inhibitory potency, systemic exposure, and DDI potential of drug metabolites was examined using the Pfizer development database to identify compounds with pre-existing in vivo biotransformation data, where circulating metabolites were identified in humans. The database yielded 33 structurally diverse compounds with collectively 115 distinct circulating metabolites. Of these, 52% (60/115) achieved exposures >25% of parent drug levels as judged from mass balance/metabolite identification studies. It was noted that 14 metabolite standards for 12 parent drugs had been synthesized, monitored in clinical studies, and examined for cytochrome P450 inhibition. For the 14 metabolite/parent drug pairs, no clinically relevant DDIs were expected to occur against the major human cytochrome P450 isoforms. A review of the literature for parent/metabolite DDI information was also conducted to examine trends using a larger data set. Leveraging the analysis of both internal and literature-based data sets, an algorithm was devised for use in drug discovery/early development to assess cytochrome P450 inhibitory potential of drug metabolites and the propensity to cause a clinically relevant DDI.