Multiple Inhibition Mechanisms and Prediction of Drug–Drug Interactions: Status of Metabolism and Transporter Models as Exemplified by Gemfibrozil–Drug Interactions

Quantitative prediction of CYP3A induction-mediated drug-drug interactions in clinical practice

There have been no reports on the quantitative prediction of CYP3A induction-mediated decreases in AUC and Cmax for drug candidates identified as a "victims" of CYP3A induction. Our previous study separately evaluated the fold-induction of hepatic and intestinal CYP3A by known inducers using clinical induction data and revealed that we were able to quantitatively predict the AUC ratio (AUCR) of a few CYP3A substrates in the presence and absence of CYP3A inducers. In the present study, we investigate the predictability of AUCR and also Cmax ratio (CmaxR) in additional 54 clinical studies. The fraction metabolized by CYP3A (fm), the intestinal bioavailability (Fg), and the hepatic intrinsic clearance (CLint) of substrates were determined by the in vitro experiments as well as clinical data used for calculating AUCR and CmaxR. The result showed that 65-69% and 65-67% of predictions were within 2-fold of observed AUCR and CmaxR, respectively. A simulation using multiple parameter combinations suggested that the variability of fm and Fg within a certain range might have a minimal impact on the calculation output. These findings suggest that clinical AUCR and CmaxR of CYP3A substrates can be quantitatively predicted from the preclinical stage.

Machine Learning Models Identify New Inhibitors for Human OATP1B1

The uptake transporter OATP1B1 (SLC01B1) is largely localized to the sinusoidal membrane of hepatocytes and is a known victim of unwanted drug-drug interactions. Computational models are useful for identifying potential substrates and/or inhibitors of clinically relevant transporters. Our goal was to generate OATP1B1 in vitro inhibition data for [3H] estrone-3-sulfate (E3S) transport in CHO cells and use it to build machine learning models to facilitate a comparison of seven different classification models (Deep learning, Adaboosted decision trees, Bernoulli naïve bayes, k-nearest neighbors (knn), random forest, support vector classifier (SVC), logistic regression (lreg), and XGBoost (xgb)] using ECFP6 fingerprints to perform 5-fold, nested cross validation. In addition, we compared models using 3D pharmacophores, simple chemical descriptors alone or plus ECFP6, as well as ECFP4 and ECFP8 fingerprints. Several machine learning algorithms (SVC, lreg, xgb, and knn) had excellent nested cross validation statistics, particularly for accuracy, AUC, and specificity. An external test set containing 207 unique compounds not in the training set demonstrated that at every threshold SVC outperformed the other algorithms based on a rank normalized score. A prospective validation test set was chosen using prediction scores from the SVC models with ECFP fingerprints and were tested in vitro with 15 of 19 compounds (84% accuracy) predicted as active (≥20% inhibition) showed inhibition. Of these compounds, six (abamectin, asiaticoside, berbamine, doramectin, mobocertinib, and umbralisib) appear to be novel inhibitors of OATP1B1 not previously reported. These validated machine learning models can now be used to make predictions for drug-drug interactions for human OATP1B1 alongside other machine learning models for important drug transporters in our MegaTrans software.

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.

A computational approach to predict multi-pathway drug-drug interactions: A case study of irinotecan, a colon cancer medication

Drug-drug interactions (DDIs) are a potentially distressing corollary of drug interventions, and may result in discomfort, debilitating illness, or even death. Existing research predominantly considers only a single level of interaction; however, serious health complications may result from multi-pathway DDIs, and so new methods are needed to enable predicting and preventing complex DDIs. This article introduces a novel method for the prediction of DDIs at two pharmacological levels (metabolic and transporter interactions) by means of a rule-based model implemented with Semantic Web technologies. The chemotherapy agent irinotecan is used as a case study for demonstrating the validity of this approach. Mechanistic and interaction data were mined from available sources and then used to predict interactors of irinotecan, including potential DDIs mediated by previously unidentified mechanisms. The findings also draw attention to the profound variation between DDI resources, indicating that clinical practice would see significant value from the development of an evidence-based resource to support DDI identification.

In vitro-in vivo correlation of the drug-drug interaction potential of antiretroviral HIV treatment regimens on CYP1A1 substrate riociguat

Objectives: Riociguat is a soluble guanylate cyclase stimulator licensed for the treatment of pulmonary arterial hypertension (PAH), a potentially fatal complication of human immunodeficiency virus infection. This study investigated the inhibitory potency of selected antiretroviral regimens on the metabolic clearance of riociguat.Methods: The inhibitory potential of the components of six antiretroviral combinations (ATRIPLA® (efavirenz/emtricitabine/tenofovir disoproxil), COMPLERA® (rilpivirine/emtricitabine/tenofovir disoproxil), STRIBILD® (elvitegravir/cobicistat/emtricitabine/tenofovir disoproxil), TRIUMEQ® (abacavir/dolutegravir/lamivudine), and two ritonavir-boosted regimens) on riociguat metabolism were evaluated in recombinant human CYP1A1 and CYP3A4 as well as in human hepatocytes exhibiting both CYP1A1 and CYP3A4 activity. In vitro-in vivo correlation was performed between calculated and observed increases in riociguat exposure in vivo.Results: Using both in vitro systems, the predicted increase in exposure of riociguat was highest with components of TRIUMEQ® followed by COMPLERA®, ATRIPLA®, STRIBILD®, and the ritonavir-boosted regimens. Further experiments in human hepatocytes confirmed CYP1A1 to be the predominant enzyme in the metabolic clearance of riociguat.Conclusion: Antiretroviral treatment containing the potent CYP1A1 inhibitor abacavir had the greatest impact on riociguat metabolic clearance. The impact of comedications containing only strong CYP3A4 inhibitors e.g. ritonavir was less pronounced, suggesting a benefit of riociguat over PAH-targeting medications with contraindications for use with strong CYP3A4 inhibitors.

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.

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.

Disposition pathway-dependent approach for predicting organic anion-transporting polypeptide-mediated drug-drug interactions

BACKGROUND AND OBJECTIVE

Organic anion-transporting polypeptide (OATP)-mediated drug-drug interactions (DDIs) are among the most important classes of clinically relevant DDIs. Accurate prediction of the OATP-mediated DDIs is not successful due to the sequential disposition pathways of OATP substrates in humans. Intestinal and hepatic uptake transporters, efflux transporters, and cytochrome P450 (CYP) enzymes are often involved in the sequential disposition pathways of typical OATP substrates. The aim of this proof-of-concept study is to develop and validate a novel approach which can be used to predict OATP-mediated DDIs with significantly increased accuracy and decreased false-negatives.

METHODS

The feasibility of using a disposition pathway-dependent prediction (DPDP) approach to predict the ratios of the area under the plasma concentration-time curve (AUC(R)) in the presence and absence of the inhibitor was investigated. A total of 62 clinical DDI studies were included in this feasibility study. The disposition pathways governing the outcome of DDIs were first identified for each substrate using the information within learning sets, and then substrate-specific algorithms were used to predict the DDI risks of the external validation set (51 DDIs).

RESULTS

The method predicted AUC(R) within 50-200 % for 50 studies (98 %), and the false-negative rate was 9.8 %. The DPDP approach showed significant improvement over an existing approach and was used to forecast the magnitude of 198 DDIs that have not been studied.

CONCLUSION

This approach can be used to avoid unnecessary clinical DDI studies during new drug development.

Predicting drug-drug interactions: application of physiologically based pharmacokinetic models under a systems biology approach

The development of in vitro-in vivo extrapolation (IVIVE), a 'bottom-up' approach, to predict pharmacokinetic parameters and drug-drug interactions (DDIs) has accelerated mainly due to an increase in the understanding of the multiple mechanisms involved in these interactions and the availability of appropriate in vitro systems that act as surrogates for delineating various elements of the interactions relevant to absorption, distribution, metabolism and elimination. Recent advances in the knowledge of the population variables required for IVIVE (demographic, anatomical, genetic and physiological parameters) have also contributed to the appreciation of the sources of variability and wider use of this approach for different scenarios within the pharmaceutical industry. Initially, the authors present an overview of the integration of IVIVE into 'static' and 'dynamic' models for the quantitative prediction of DDIs. The main purpose of this review is to discuss the application of IVIVE in conjunction with physiologically based pharmacokinetic modeling under a systems biology approach to characterize the potential DDIs in individual patients, including those who cannot be investigated in formal clinical trials for ethical reasons. In addition, we address the issues related to the prediction of complex DDIs involving the inhibition of cytochrome P- and transporter-mediated activities through multiple drugs.

Stereoselective inhibition of CYP2C19 and CYP3A4 by fluoxetine and its metabolite: implications for risk assessment of multiple time-dependent inhibitor systems

Recent guidance on drug-drug interaction (DDI) testing recommends evaluation of circulating metabolites. However, there is little consensus on how to quantitatively predict and/or assess the risk of in vivo DDIs by multiple time-dependent inhibitors (TDIs) including metabolites from in vitro data. Fluoxetine was chosen as the model drug to evaluate the role of TDI metabolites in DDI prediction because it is a TDI of both CYP3A4 and CYP2C19 with a circulating N-dealkylated inhibitory metabolite, norfluoxetine. In pooled human liver microsomes, both enantiomers of fluoxetine and norfluoxetine were TDIs of CYP2C19, (S)-norfluoxetine was the most potent inhibitor with time-dependent inhibition affinity constant (KI) of 7 μM, and apparent maximum time-dependent inhibition rate (k(inact,app)) of 0.059 min(-1). Only (S)-fluoxetine and (R)-norfluoxetine were TDIs of CYP3A4, with (R)-norfluoxetine being the most potent (K(I) = 8 μM, and k(inact,app) = 0.011 min(-1)). Based on in-vitro-to-in-vivo predictions, (S)-norfluoxetine plays the most important role in in vivo CYP2C19 DDIs, whereas (R)-norfluoxetine is most important in CYP3A4 DDIs. Comparison of two multiple TDI prediction models demonstrated significant differences between them in in-vitro-to-in-vitro predictions but not in in-vitro-to-in-vivo predictions. Inclusion of all four inhibitors predicted an in vivo decrease in CYP2C19 (95%) and CYP3A4 (60-62%) activity. The results of this study suggest that adequate worst-case risk assessment for in vivo DDIs by multiple TDI systems can be achieved by incorporating time-dependent inhibition by both parent and metabolite via simple addition of the in vivo time-dependent inhibition rate/cytochrome P450 degradation rate constant (λ/k(deg)) values, but quantitative DDI predictions will require a more thorough understanding of TDI mechanisms.

ATP-dependent transport of statins by human and rat MRP2/Mrp2

Multidrug resistance associated protein-2, MRP2 (human), Mrp2 (rat) are an efflux transporter, responsible for the transport of numerous endogenous and xenobiotic compounds including taurocholate, methotrexate and carboxydichlorofluorescein (CDF). The present study aims to characterise transport of statins by human and rat MRP2/Mrp2 using membrane and vesicle preparations. All statins tested (simvastatin, pravastatin, pitavastatin, fluvastatin, atorvastatin, lovastatin and rosuvastatin) stimulated vanadate-sensitive ATPase activity in membranes expressing human or rat MRP2/Mrp2, suggesting that all statins are substrates of human and rat MRP2/Mrp2. The substrate affinity (Km) of all statins for MRP2/Mrp2 was comparable and no correlation between lipophilicity (logD7.0) and Km was seen. All statins also inhibited uptake of the fluorescent Mrp2 substrate, CDF (1μM) into vesicles expressing human or rat MRP2/Mrp2 with similar IC50 values. Fitting of the inhibitory data to the hill slope equation, gave hill coefficients (h) of greater than one, suggesting that transport involved more than one binding site for inhibitors of MPR2 and Mrp2. We conclude that statins were transported by both human and rat MRP2/Mrp2 with similar affinity. Statins were also shown to compete with other substrates for transport by MRP2/Mrp2 and that this transport involved more than one binding site on the Mrp2/MRP2 protein.

ITC recommendations for transporter kinetic parameter estimation and translational modeling of transport-mediated PK and DDIs in humans

This white paper provides a critical analysis of methods for estimating transporter kinetics and recommendations on proper parameter calculation in various experimental systems. Rational interpretation of transporter-knockout animal findings and application of static and dynamic physiologically based modeling approaches for prediction of human transporter-mediated pharmacokinetics and drug-drug interactions (DDIs) are presented. The objective is to provide appropriate guidance for the use of in vitro, in vivo, and modeling tools in translational transporter science.

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

PURPOSE

Quantitative prediction of complex drug-drug interactions (DDIs) is challenging. Repaglinide is mainly metabolized by cytochrome-P-450 (CYP)2C8 and CYP3A4, and is also a substrate of organic anion transporting polypeptide (OATP)1B1. The purpose is to develop a physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics and DDIs of repaglinide.

METHODS

In vitro hepatic transport of repaglinide, gemfibrozil and gemfibrozil 1-O-β-glucuronide was characterized using sandwich-culture human hepatocytes. A PBPK model, implemented in Simcyp (Sheffield, UK), was developed utilizing in vitro transport and metabolic clearance data.

RESULTS

In vitro studies suggested significant active hepatic uptake of repaglinide. Mechanistic model adequately described repaglinide pharmacokinetics, and successfully predicted DDIs with several OATP1B1 and CYP3A4 inhibitors (<10% error). Furthermore, repaglinide-gemfibrozil interaction at therapeutic dose was closely predicted using in vitro fraction metabolism for CYP2C8 (0.71), when primarily considering reversible inhibition of OATP1B1 and mechanism-based inactivation of CYP2C8 by gemfibrozil and gemfibrozil 1-O-β-glucuronide.

CONCLUSIONS

This study demonstrated that hepatic uptake is rate-determining in the systemic clearance of repaglinide. The model quantitatively predicted several repaglinide DDIs, including the complex interactions with gemfibrozil. Both OATP1B1 and CYP2C8 inhibition contribute significantly to repaglinide-gemfibrozil interaction, and need to be considered for quantitative rationalization of DDIs with either drug.

Cynomolgus monkey as a potential model to assess drug interactions involving hepatic organic anion transporting polypeptides: in vitro, in vivo, and in vitro-to-in vivo extrapolation

Organic anion-transporting polypeptides (OATP) 1B1, 1B3, and 2B1 can serve as the loci of drug-drug interactions (DDIs). In the present work, the cynomolgus monkey was evaluated as a potential model for studying OATP-mediated DDIs. Three cynomolgus monkey OATPs (cOATPs), with a high degree of amino acid sequence identity (91.9, 93.5, and 96.6% for OATP1B1, 1B3, and 2B1, respectively) to their human counterparts, were cloned, expressed, and characterized. The cOATPs were stably transfected in human embryonic kidney cells and were functionally similar to the corresponding human OATPs (hOATPs), as evident from the similar uptake rate of typical substrates (estradiol-17β-d-glucuronide, cholecystokinin octapeptide, and estrone-3-sulfate). Moreover, six known hOATP inhibitors exhibited similar IC(50) values against cOATPs. To further evaluate the appropriateness of the cynomolgus monkey as a model, a known hOATP substrate [rosuvastatin (RSV)]-inhibitor [rifampicin (RIF)] pair was examined in vitro; the monkey-derived parameters (RSV K(m) and RIF IC(50)) were similar (within 3.5-fold) to those obtained with hOATPs and human primary hepatocytes. In vivo, the area under the plasma concentration-time curve of RSV (3 mg/kg, oral) given 1 hour after a single RIF dose (15 mg/kg, oral) was increased 2.9-fold in cynomolgus monkeys, consistent with the value (3.0-fold) reported in humans. A number of in vitro-in vivo extrapolation approaches, considering the fraction of the pathways affected and free versus total inhibitor concentrations, were also explored. It is concluded that the cynomolgus monkey has the potential to serve as a useful model for the assessment of OATP-mediated DDIs in a nonclinical setting.

Cyclosporine inhibition of hepatic and intestinal CYP3A4, uptake and efflux transporters: application of PBPK modeling in the assessment of drug-drug interaction potential

PURPOSE

To apply physiologically-based pharmacokinetic (PBPK) modeling to investigate the consequences of reduction in activity of hepatic and intestinal uptake and efflux transporters by cyclosporine and its metabolite AM1.

METHODS

Inhibitory potencies of cyclosporine and AM1 against OATP1B1, OATP1B3 and OATP2B1 were investigated in HEK293 cells +/- pre-incubation. Cyclosporine PBPK model implemented in Matlab was used to assess interaction potential (+/- metabolite) against different processes (uptake, efflux and metabolism) in liver and intestine and to predict quantitatively drug-drug interaction with repaglinide.

RESULTS

Cyclosporine and AM1 were potent inhibitors of OATP1B1 and OATP1B3, IC(50) ranging from 0.019-0.093 μM following pre-incubation. Cyclosporine PBPK model predicted the highest interaction potential against liver uptake transporters, with a maximal reduction of >70% in OATP1B1 activity; the effect on hepatic efflux and metabolism was minimal. In contrast, 80-97% of intestinal P-gp and CYP3A4 activity was reduced due to the 50-fold higher cyclosporine enterocytic concentrations relative to unbound hepatic inlet. The inclusion of AM1 resulted in a minor increase in the predicted maximal reduction of OATP1B1/1B3 activity. Good predictability of cyclosporine-repaglinide DDI and the impact of dose staggering are illustrated.

CONCLUSIONS

This study highlights the application of PBPK modeling for quantitative prediction of transporter-mediated DDIs with concomitant consideration of P450 inhibition.

Importance of multi-p450 inhibition in drug-drug interactions: evaluation of incidence, inhibition magnitude, and prediction from in vitro data

Drugs that are mainly cleared by a single enzyme are considered more sensitive to drug-drug interactions (DDIs) than drugs cleared by multiple pathways. However, whether this is true when a drug cleared by multiple pathways is coadministered with an inhibitor of multiple P450 enzymes (multi-P450 inhibition) is not known. Mathematically, simultaneous equipotent inhibition of two elimination pathways that each contribute half of the drug clearance is equal to equipotent inhibition of a single pathway that clears the drug. However, simultaneous strong or moderate inhibition of two pathways by a single inhibitor is perceived as an unlikely scenario. The aim of this study was (i) to identify P450 inhibitors currently in clinical use that can inhibit more than one clearance pathway of an object drug in vivo and (ii) to evaluate the magnitude and predictability of DDIs caused by these multi-P450 inhibitors. Multi-P450 inhibitors were identified using the Metabolism and Transport Drug Interaction Database. A total of 38 multi-P450 inhibitors, defined as inhibitors that increased the AUC or decreased the clearance of probes of two or more P450s, were identified. Seventeen (45%) multi-P450 inhibitors were strong inhibitors of at least one P450, and an additional 12 (32%) were moderate inhibitors of one or more P450s. Only one inhibitor (fluvoxamine) was a strong inhibitor of more than one enzyme. Fifteen of the multi-P450 inhibitors also inhibit drug transporters in vivo, but such data are lacking on many of the inhibitors. Inhibition of multiple P450 enzymes by a single inhibitor resulted in significant (>2-fold) clinical DDIs with drugs that are cleared by multiple pathways such as imipramine and diazepam, while strong P450 inhibitors resulted in only weak DDIs with these object drugs. The magnitude of the DDIs between multi-P450 inhibitors and diazepam, imipramine, and omeprazole could be predicted using in vitro data with similar accuracy as probe substrate studies with the same inhibitors. The results of this study suggest that inhibition of multiple clearance pathways in vivo is clinically relevant, and the risk of DDIs with object drugs may be best evaluated in studies using multi-P450 inhibitors.

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

The plasma concentration of repaglinide is reported to increase greatly when given after repeated oral administration of itraconazole and gemfibrozil. The present study analyzed this interaction based on a physiologically based pharmacokinetic (PBPK) model incorporating inhibition of the hepatic uptake transporter and metabolic enzymes involved in repaglinide disposition. Firstly, the plasma concentration profiles of inhibitors (itraconazole, gemfibrozil, and gemfibrozil glucuronide) were reproduced by a PBPK model to obtain their pharmacokinetic parameters. The plasma concentration profiles of repaglinide were then analyzed by a PBPK model, together with those of the inhibitors, assuming a competitive inhibition of CYP3A4 by itraconazole, mechanism-based inhibition of CYP2C8 by gemfibrozil glucuronide, and inhibition of organic anion transporting polypeptide (OATP) 1B1 by gemfibrozil and its glucuronide. The plasma concentration profiles of repaglinide were well reproduced by the PBPK model based on the above assumptions, and the optimized values for the inhibition constants (0.0676 nM for itraconazole against CYP3A4; 14.2 μM for gemfibrozil against OATP1B1; and 5.48 μM for gemfibrozil glucuronide against OATP1B1) and the fraction of repaglinide metabolized by CYP2C8 (0.801) were consistent with the reported values. The validity of the obtained parameters was further confirmed by sensitivity analyses and by reproducing the repaglinide concentration increase produced by concomitant gemfibrozil administration at various timings/doses. The present findings suggested that the reported concentration increase of repaglinide, suggestive of synergistic effects of the coadministered inhibitors, can be quantitatively explained by the simultaneous inhibition of the multiple clearance pathways of repaglinide.

In vitro investigations into the roles of drug transporters and metabolizing enzymes in the disposition and drug interactions of dolutegravir, a HIV integrase inhibitor

Dolutegravir (DTG; S/GSK1349572) is a potent HIV-1 integrase inhibitor with a distinct resistance profile and a once-daily dose regimen that does not require pharmacokinetic boosting. This work investigated the in vitro drug transport and metabolism of DTG and assessed the potential for clinical drug-drug interactions. DTG is a substrate for the efflux transporters P-glycoprotein (Pgp) and human breast cancer resistance protein (BCRP). Its high intrinsic membrane permeability limits the impact these transporters have on DTG's intestinal absorption. UDP-glucuronosyltransferase (UGT) 1A1 is the main enzyme responsible for the metabolism of DTG in vivo, with cytochrome P450 (P450) 3A4 being a notable pathway and UGT1A3 and UGT1A9 being only minor pathways. DTG demonstrated little or no inhibition (IC(50) values > 30 μM) in vitro of the transporters Pgp, BCRP, multidrug resistance protein 2, organic anion transporting polypeptide 1B1/3, organic cation transporter (OCT) 1, or the drug metabolizing enzymes CYP1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 3A4, UGT1A1, or 2B7. Further, DTG did not induce CYP1A2, 2B6, or 3A4 mRNA in vitro using human hepatocytes. DTG does inhibit the renal OCT2 (IC(50) = 1.9 μM) transporter, which provides a mechanistic basis for the mild increases in serum creatinine observed in clinical studies. These in vitro studies demonstrate a low propensity for DTG to be a perpetrator of clinical drug interactions and provide a basis for predicting when other drugs could result in a drug interaction with DTG.

Transporter-mediated drug--drug interactions involving OATP substrates: predictions based on in vitro inhibition studies

Transporter-mediated drug–drug interactions (DDIs) are among the most important of the clinically relevant pharmacokinetic DDIs. We investigated the validity of a static prediction of area under the plasma concentration-time curve (AUC) ratios (AUCRs; AUC(with inhibitor)/AUC(control) using in vitro inhibition profiles, and selected the types of assumptions that improved the prediction accuracy with minimizing false-negative predictions. We used data from 58 DDI studies involving 12 substrates of hepatic organic anion–transporting polypeptides (OATPs). With original assumptions regarding the maximal increase in intestinal availability, maximum unbound concentration at the inlet to the liver, and inhibition of only the hepatic uptake process, the predicted AUCRs were comparable to those reported within a two/threefold error margin in 44/52 studies, whereas in 16 studies, the predictions were judged to be falsenegatives. When the inhibitory effects on both hepatic uptake and efflux/metabolisms were considered, the overall prediction accuracy became worse, although the false-negative prediction decreased to 11 studies. This illustrates that if appropriate assumptions are selected, unnecessary clinical DDI studies can be reasonably avoided.

Current understanding of hepatic and intestinal OATP-mediated drug-drug interactions

At present, many patients are medicated with various drugs, which are, at the same time, associated with an increased risk of drug-drug interactions (DDIs). Detailed analysis of mechanisms underlying DDIs is the basis of a better prediction of adverse drug events caused by drug interactions. In the last few decades, an involvement of transporters in such processes has been more and more recognized. Indeed, uptake transporters belonging to the organic anion-transporting polypeptide (OATP) family have been shown to interact with a variety of drugs in clinical use. Particularly, the subfamily of OATP1B transporters has been extensively studied, identifying several clinical significant DDIs based on those hepatic uptake transporters. By contrast, the role of OATP2B1 in this context is rather underestimated. Therefore, in addition to known interactions based on OATP1B transporters, we have focused on DDIs probably based on OATP2B1 inhibition in the liver and those possibly owing to the inhibition of OATP2B1-mediated drug absorption in the intestine.

Prediction of the in vivo OATP1B1-mediated drug-drug interaction potential of an investigational drug against a range of statins

To support drug development, the drug-drug interaction potential (DDI) of an investigational drug (AZX) was assessed against the probe estradiol 17β-glucuronide as well as against simvastatin acid, atorvastatin, pravastatin, pitavastatin, fluvastatin, rosuvastatin and estrone 3-sulfate. The inhibitory potentials of the OATP1B1 inhibitors rifamycin SV and gemfibrozil were assessed in parallel. Monolayer cellular uptake assays were used to determine inhibition of human OATP1B1. Apparent K(m) values for the OATP1B1-mediated transport of [(3)H] substrates were determined prior to their use as probes in inhibition studies, and ranged from 0.6 to 29 μM for statins. The K(m) of lipophilic simvastatin acid could not be determined due to its high passive permeability that masked OATP1B1 transport, and therefore this statin could not be used as a probe. Estrone 3-sulfate exhibited biphasic kinetics, whereas estradiol 17β-glucuronide demonstrated simple Michaelis-Menton kinetics. AZX moderately inhibited OATP1B1-mediated transport of all statins (IC(50)=4.6-9.7 μM), except fluvastatin, of estradiol 17β-glucuronide (IC(50)=5.3 μM), and weakly inhibited estrone 3-sulfate (IC(50)=79 μM). Rifamycin SV strongly, and gemfibrozil weakly, inhibited the OATP1B1-mediated transport of substrates. Estradiol 17β-glucuronide was identified as a good surrogate probe for statins when assessing OATP1B1 inhibitory potential using this test system. Inhibition data was used to predict the likelihood of a clinical DDI, using current draft US FDA guidance and recommendations of the International Transporter Consortium. Predictions for AZX indicated the potential for an OATP1B1-mediated DDI in vivo and that a clinical interaction study is warranted to confirm whether AZX is an OATP1B1 inhibitor in the clinic.

In vitro-to-in vivo predictions of drug-drug interactions involving multiple reversible inhibitors

INTRODUCTION

Predictions of drug-drug interactions (DDIs) are commonly performed for single inhibitors, but interactions involving multiple inhibitors also frequently occur. Predictions of such interactions involving stereoisomer pairs, parent/metabolite combinations and simultaneously administered multiple inhibitors are increasing in importance. This review provides the framework for predicting inhibitory DDIs of multiple inhibitors with any combination of reversible inhibition mechanism.

AREAS COVERED

The review provides an overview of the reliability of the in vitro determined reversible inhibition mechanism. Furthermore, the article provides a method to predict DDIs for multiple reversible inhibitors that allows substituting the inhibition constant (K(i)) with an inhibitor affinity (IC(50)) value determined at S << K(M).

EXPERT OPINION

A better understanding and the prediction methods of DDIs, resulting from multiple inhibitors, are important. The inhibition mechanism of a reversible inhibitor is often equivocal across studies and unreliable. Determination of the K(i) requires the assignment of reversible inhibition mechanism but in vitro-to-in vivo prediction of DDI risk can be achieved for multiple inhibitors from estimates of the inhibitor affinity (IC(50)) only, regardless of the inhibition mechanism.

The evolution of the OATP hepatic uptake transport protein family in DMPK sciences: from obscure liver transporters to key determinants of hepatobiliary clearance

Over the last two decades the impact on drug pharmacokinetics of the organic anion transporting polypeptides (OATPs: OATP-1B1, 1B3 and 2B1), expressed on the sinusoidal membrane of the hepatocyte, has been increasingly recognized. OATP-mediated uptake into the hepatocyte coupled with subsequent excretion into bile via efflux proteins, such as MRP2, is often referred to as hepatobiliary excretion. OATP transporter proteins can impact some drugs in several ways including pharmacokinetic variability, pharmacodynamic response and drug-drug interactions (DDIs). The impact of transporter mediated hepatic clearance is illustrated with case examples, from the literature and also from the Pfizer portfolio. The currently available in vitro techniques to study the hepatic transporter proteins involved in the hepatobiliary clearance of drugs are reviewed herein along with recent advances in using these in vitro data to predict the human clearance of compounds recognized by hepatic uptake transporters.

Repaglinide-gemfibrozil drug interaction: inhibition of repaglinide glucuronidation as a potential additional contributing mechanism

AIM

To further explore the mechanism underlying the interaction between repaglinide and gemfibrozil, alone or in combination with itraconazole.

METHODS

Repaglinide metabolism was assessed in vitro (human liver subcellular fractions, fresh human hepatocytes, and recombinant enzymes) and the resulting incubates were analyzed, by liquid chromatography-mass spectrometry (LC-MS) and radioactivity counting, to identify and quantify the different metabolites therein. Chemical inhibitors, in addition to a trapping agent, were also employed to elucidate the importance of each metabolic pathway. Finally, a panel of human liver microsomes (genotyped for UGT1A1*28 allele status) was used to determine the importance of UGT1A1 in the direct glucuronidation of repaglinide.

RESULTS

The results of the present study demonstrate that repaglinide can undergo direct glucuronidation, a pathway that can possibly contribute to the interaction with gemfibrozil. For example, [³H]-repaglinide formed glucuronide and oxidative metabolites (M2 and M4) when incubated with primary human hepatocytes. Gemfibrozil effectively inhibited (∼78%) both glucuronide and M4 formation, but had a minor effect on M2 formation. Concomitantly, the overall turnover of repaglinide was also inhibited (∼80%), and was completely abolished when gemfibrozil was co-incubated with itraconazole. These observations are in qualitative agreement with the in vivo findings. UGT1A1 plays a significant role in the glucuronidation of repaglinide. In addition, gemfibrozil and its glucuronide inhibit repaglinide glucuronidation and the inhibition by gemfibrozil glucuronide is time-dependent.

CONCLUSIONS

Inhibition of UGT enzymes, especially UGT1A1, by gemfibrozil and its glucuronide is an additional mechanism to consider when rationalizing the interaction between repaglinide and gemfibrozil.

Assessment of algorithms for predicting drug-drug interactions via inhibition mechanisms: comparison of dynamic and static models

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

The prediction of drug-drug interactions (DDIs) from in vitro data usually utilizes an average dosing interval estimate of inhibitor concentration in an equation-based static model. Simcyp®, a population-based ADME simulator, is becoming widely used for the prediction of DDIs and has the ability to incorporate the time-course of inhibitor concentration and hence generate a temporal profile of the inhibition process within a dynamic model.

WHAT THIS PAPER ADDS

Prediction of DDIs for 35 clinical studies incorporating a representative range of drug-drug interactions, with multiple studies across different inhibitors and victim drugs. Assessment of whether the inclusion of the time course of inhibition in the dynamic model improves prediction in comparison with the static model. Investigation of the impact of different inhibitor and victim drug parameters on DDI prediction accuracy including dosing time and the inclusion of active metabolites. Assessment of ability of the dynamic model to predict inter-individual variability in the DDI magnitude.

AIMS

Static and dynamic models (incorporating the time course of the inhibitor) were assessed for their ability to predict drug-drug interactions (DDIs) using a population-based ADME simulator (Simcyp®V8). The impact of active metabolites, dosing time and the ability to predict inter-individual variability in DDI magnitude were investigated using the dynamic model.

METHODS

Thirty-five in vivo DDIs involving azole inhibitors and benzodiazepines were predicted using the static and dynamic model; both models were employed within Simcyp for consistency in parameters. Simulations comprised of 10 trials with matching population demographics and dosage regimen to the in vivo studies. Predictive utility of the static and dynamic model was assessed relative to the inhibitor or victim drug investigated.

RESULTS

Use of the dynamic and static models resulted in comparable prediction success, with 71 and 77% of DDIs predicted within two-fold, respectively. Over 40% of strong DDIs (>five-fold AUC increase) were under-predicted by both models. Incorporation of the itraconazole metabolite into the dynamic model resulted in increased prediction accuracy of strong DDIs (80% within two-fold). Bias and imprecision in prediction of triazolam DDIs were higher in comparison with midazolam and alprazolam; >50% of triazolam DDIs were under-predicted regardless of the model used. Predicted inter-individual variability in the AUC ratio (coefficient of variation of 45%) was consistent with the observed variability (50%).

CONCLUSIONS

High prediction accuracy was observed using both the Simcyp dynamic and static models. The differences observed with the dose staggering and the incorporation of active metabolite highlight the importance of these variables in DDI prediction.

Potential role of intestinal first-pass metabolism in the prediction of drug-drug interactions

BACKGROUND

The contribution of intestine to the magnitude of drug-drug interactions (DDI) may be significant, considering high levels of inhibitors in the gut lumen achieved during absorption and the abundance of metabolic enzymes in the mature enterocytes. Intestinal inhibition is incorporated in the DDI prediction models as the ratio of the intestinal wall availability in the presence and absence of the inhibitor (F(G)(') and F(G), respectively).

OBJECTIVE

This review will focus on the ability of the current approaches to estimate the extent of intestinal DDI accurately, addressing predominantly the most abundant intestinal P450 enzyme, CYP3A4.

METHODS

Considering the sensitivity of the DDI prediction models to the accuracy of the F(G) estimates, the current study focuses on 3 different in vitro and in vivo approaches to assess this parameter.

RESULTS/CONCLUSION

The advantages and limitations of each of F(G) methods are outlined. Accurate assessment of this parameter is essential for the prediction of human drug clearance and drug-drug interaction potential.