PBPK Modeling of Coproporphyrin I as an Endogenous Biomarker for Drug Interactions Involving Inhibition of Hepatic OATP1B1 and OATP1B3

Comprehensive Evaluation of OATP- and BCRP-Mediated Drug-Drug Interactions of Methotrexate Using Physiologically-Based Pharmacokinetic Modeling

Methotrexate (MTX) is an antifolate agent widely used for treating conditions such as rheumatoid arthritis and hematologic cancer. This study aimed to quantitatively interpret the drug-drug interactions (DDIs) of MTX mediated by drug transporters using physiologically-based pharmacokinetic (PBPK) modeling. An open-label, randomized, 4-treatment, 6-sequence, 4-period crossover study was conducted to investigate the effects of rifampicin (RFP), an inhibitor of organic anionic transporting peptides (OATP) 1B1/3, and febuxostat (FBX), an inhibitor of breast cancer resistance protein (BCRP), on the pharmacokinetics of MTX in healthy volunteers. PBPK models of MTX, RFP, and FBX were developed based on in vitro and in vivo data, and the performance of the simulation results for final PBPK models was validated in a clinical study. In the clinical study, when MTX was co-administered with RFP or FBX, systemic exposure of MTX increased by 33% and 17%, respectively, compared with that when MTX was administered alone. When MTX was co-administered with RFP and FBX, systemic exposure increased by 52% compared with that when MTX was administered alone. The final PBPK model showed a good prediction performance for the observed clinical data. The PBPK model of MTX was well developed in this study and can be used as a potential mechanistic model to predict and evaluate drug transporter-mediated DDIs of MTX with other drugs.

Utilising Endogenous Biomarkers in Drug Development to Streamline the Assessment of Drug-Drug Interactions Mediated by Renal Transporters: A Pharmaceutical Industry Perspective

The renal secretion of many drugs is facilitated by membrane transporters, including organic cation transporter 2, multidrug and toxin extrusion protein 1/2-K and organic anion transporters 1 and 3. Inhibition of these transporters can reduce renal excretion of drugs and thereby pose a safety risk. Assessing the risk of inhibition of these membrane transporters by investigational drugs remains a key focus in the evaluation of drug-drug interactions (DDIs). Current methods to predict DDI risk are based on generating in vitro data followed by a clinical assessment using a recommended exogenous probe substrate for the individual drug transporter. More recently, monitoring plasma-based and urine-based endogenous biomarkers to predict transporter-mediated DDIs in early phase I studies represents a promising approach to facilitate, improve and potentially avoid conventional clinical DDI studies. This perspective reviews the evidence for use of these endogenous biomarkers in the assessment of renal transporter-mediated DDI, evaluates how endogenous biomarkers may help to expand the DDI assessment toolkit and offers some potential knowledge gaps. A conceptual framework for assessment that may complement the current paradigm of predicting the potential for renal transporter-mediated DDIs is outlined.

Dose Optimization Informed by PBPK Modeling: State-of-the Art and Future

Model-informed drug development (MIDD) is a powerful quantitative approach that plays an integral role in drug development and regulatory review. While applied throughout the life cycle of the development of new drugs, a key application of MIDD is to inform clinical trial design including dose selection and optimization. To date, physiologically-based pharmacokinetic (PBPK) modeling, an established component of the MIDD toolkit, has mainly been used for assessment of drug-drug interactions (DDIs) and consequential dose adjustments in regulatory submissions. As a result of recent scientific advances and growing confidence in the utility of the approach, PBPK models are being increasingly applied to provide dose recommendations for subjects with differing ages, genetics, and disease states. In this review, we present our perspective on the current landscape of regulatory acceptance of PBPK applications supported by relevant case studies. We also discuss the recent progress and future challenges associated with expanding the utility of PBPK models into emerging areas for regulatory decision making, especially dose optimization in highly vulnerable and understudied populations and facilitating diversity in clinical trials.

Membrane transporters in drug development and as determinants of precision medicine

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

Utility of Coproporphyrin-I Determination in First-in-Human Study for Early Evaluation of OATP1B Inhibitory Potential Based on Investigation of Ensitrelvir, an Oral SARS-CoV-2 3C-Like Protease Inhibitor

Coproporphyrin-I (CP-I) has been investigated as an endogenous biomarker of organic anion transporting polypeptide (OATP) 1B. Here, we determined the CP-I concentrations in a cocktail drug-drug interaction (DDI) study of ensitrelvir to evaluate the OATP1B inhibitory potential because ensitrelvir had increased plasma concentrations of rosuvastatin in this study, raising concerns about breast cancer resistance protein and OATP1B inhibition. Furthermore, CP-I concentrations were compared between active and placebo groups in a first-in-human (FIH) study of ensitrelvir to verify whether the OATP1B inhibitory potential could be estimated at an early drug development stage. In the cocktail DDI study, CP-I did not differ between with/without administration of ensitrelvir, indicating that ensitrelvir has no OATP1B inhibitory effect. Although there were some individual variabilities in CP-I concentrations among the treatment groups in the FIH study, the normalization of CP-I concentrations with pre-dose values minimized these variabilities, suggesting that this normalized method would be helpful for comparing the CP-I from different participants. Finally, we concluded that CP-I concentrations were not affected by ensitrelvir in the FIH study. These results suggested that the CP-I determination in an FIH study and its normalized method can be useful for an early evaluation of the OATP1B-mediated DDI potential in humans.

Quantitative prediction of transporter-mediated drug-drug interactions using the mechanistic static pharmacokinetic (MSPK) model

Guidance/guidelines on drug-drug interactions (DDIs) have been issued in Japan, the United States, and Europe. These guidance/guidelines provide decision trees for conducting metabolizing enzyme-mediated clinical DDI studies; however, the decision trees for transporter-mediated DDIs lack quantitative prediction methods. In this study, the accuracy of a net-effect mechanistic static pharmacokinetics (MSPK) model containing the fraction transported (ft) of transporters was examined to predict transporter-mediated DDIs. This study collected information on 25 oral drugs with new active reagents that were used in clinical DDI studies as perpetrators (42 cases) from drugs approved in Japan between April 2016 and June 2020. The AUCRs (AUC ratios with and without perpetrators) of victim drugs were predicted using the net-effect MSPK model. As a result, 83 and 95% of the predicted AUCRs were within 1.5- and 2-fold error in the observed AUCRs, respectively. In cases where the victims were statins in which pharmacokinetics several transporters are involved, 70 and 91% of the predicted AUCRs were within 1.5- and 2-fold errors, respectively. Therefore, the net-effect MSPK model was applicable for predicting the AUCRs of victims, which are substrates for multiple transporters.

Physiologically-based pharmacokinetic modeling for investigating the effect of simeprevir on concomitant drugs and an endogenous biomarker of OATP1B

The orally available anti-hepatitis C virus (HCV) drug simeprevir exhibits nonlinear pharmacokinetics at the clinical doses due to saturation of cytochrome P450 (CYP) 3A4 metabolism and organic anion transporting peptide (OATP) 1B mediated hepatic uptake. Additionally, simeprevir increases exposures of concomitant drugs by CYP3A4 and OATP1B inhibition. The objective of this study was to develop physiologically-based pharmacokinetic (PBPK) models that could describe drug-drug interactions (DDIs) of simeprevir with concomitant drugs via CYP3A4 and OATP1B inhibition, and also to capture the effects on coproporphyrin-I (CP-I), an endogenous biomarker of OATP1B. PBPK modeling estimated unbound simeprevir inhibitory constant (Ki ) of 2.89 μM against CYP3A4 in the DDI results between simeprevir and midazolam in healthy volunteers. Then, we analyzed the DDIs between simeprevir and atorvastatin, a dual substrate of CYP3A4 and OATP1B, in healthy volunteers, and unbound Ki against OATP1B was estimated to be 0.00347 μM. Finally, we analyzed the increase in the blood level of CP-I by simeprevir to verify the Ki,OATP1B . Because CP-I was measured in subjects with HCV with various hepatic fibrosis state, Monte Carlo simulation was performed to involve the decreases in expression levels of hepatic CYP3A4 and OATP1B and their interindividual variabilities. The PBPK modeling coupled with Monte Carlo simulation using the Ki,OATP1B value obtained from atorvastatin study reasonably recovered the observed relationship between CP-I and simeprevir blood levels. In conclusion, the simeprevir PBPK model developed in this study can quantitatively describe the increase in exposures of concomitant drugs and an endogenous biomarker via inhibition of CYP3A4 and OATP1B.

Preincubation Time-Dependent, Long-Lasting Inhibition of Drug Transporters and Impact on the Prediction of Drug-Drug Interactions

Transporter-mediated drug-drug interaction (DDI) is of clinical concern, and the quantitative prediction of DDIs is an indispensable part of drug development. Cell-based inhibition assays, in which a representative probe substrate and a potential inhibitor are coincubated, are routinely performed to assess the inhibitory potential of new molecular entities on drug transporters. However, the inhibitory effect of cyclosporine A (CsA) on organic anion transporting polypeptide (OATP) 1B1 is substantially potentiated with CsA preincubation, and this effect is both long-lasting and dependent on the preincubation time. This phenomenon has also been reported with transporters other than OATP1Bs, but it is considered more prevalent among OATP1Bs and organic cation transporters. Regulatory agencies have also noted this preincubation effect and have recommended that pharmaceutical companies consider inhibitor preincubation when performing in vitro OATP1B1 and OATP1B3 inhibition studies. Although the underlying mechanisms responsible for the preincubation effect are not fully understood, a trans-inhibition mechanism was recently demonstrated for OATP1B1 inhibition by CsA, in which CsA inhibited OATP1B1 not only extracellularly (cis-inhibition) but also intracellularly (trans-inhibition). Furthermore, the trans-inhibition potency of CsA was much greater than that of cis-inhibition, suggesting that trans-inhibition might be a key driver of clinical DDIs of CsA with OATP1B substrate drugs. Although confidence in transporter-mediated DDI prediction is generally considered to be low, the predictability might be further improved by incorporating the trans-inhibition mechanism into static and dynamic models for preincubation-dependent inhibitors of OATP1Bs and perhaps other transporters. SIGNIFICANCE STATEMENT: Preincubation time-dependent, long-lasting inhibition has been observed for OATP1B1 and other solute carrier transporters in vitro. Recently, a trans-inhibition mechanism for the preincubation effect of CsA on OATP1B1 inhibition was identified, with the trans-inhibition potency being greater than that of cis-inhibition. The concept of trans-inhibition may allow us to further understand the mechanism of transporter-mediated DDIs not only for OATP1B1 but also for other transporters and to improve the accuracy and confidence of DDI predictions.

Progress in the Quantitative Assessment of Transporter-Mediated Drug-Drug Interactions Using Endogenous Substrates in Clinical Studies

Variations in drug transporter activities, caused by genetic polymorphism and drug-drug interactions (DDIs), alter the systemic exposure of substrate drugs, leading to differences in drug responses. Recently, some endogenous substrates of drug transporters, particularly the solute carrier family transporters such as OATP1B1, OATP1B3, OAT1, OAT3, OCT1, OCT2, MATE1, and MATE2-K, have been identified to investigate variations in drug transporters in humans. Clinical data obtained support their performance as surrogate probes in terms of specificity and reproducibility. Pharmacokinetic parameters of the endogenous biomarkers depend on the genotypes of drug transporters and the systemic exposure to perpetrator drugs. Furthermore, the development of physiologically based pharmacokinetic models for the endogenous biomarkers has enabled a top-down approach to obtain insights into the effect of perpetrators on drug transporters and to more precisely simulate the DDI with victim drugs, including probe drugs. The endogenous biomarkers can address the uncertainty in the DDI prediction in the preclinical and early phases of clinical development and have the potential to fulfill regulatory requirements. Therefore, the endogenous biomarkers should be able to predict disease effects on the variations in drug transporter activities observed in patients. This mini-review focuses on recent progress in the identification and use of the endogenous drug transporter substrate biomarkers and their application in drug development. SIGNIFICANCE STATEMENT: Advances in analytical methods have enabled the identification of endogenous substrates of drug transporters. Changes in the pharmacokinetic parameters (Cmax, AUC, or CLR) of these endogenous biomarkers relative to baseline values can serve as a quantitative index to assess variations in drug transporter activities during clinical studies and thereby provide more precise DDI predictions.

Evaluation of the Selectivity of Several Organic Anion Transporting Polypeptide 1B Biomarkers Using Relative Activity Factor Method

In recent years, some endogenous substrates of organic anion transporting polypeptide 1B (OATP1B) have been identified and characterized as potential biomarkers to assess OATP1B-mediated clinical drug-drug interactions (DDIs). However, quantitative determination of their selectivity to OATP1B is still limited. In this study, we developed a relative activity factor (RAF) method to determine the relative contribution of hepatic uptake transporters OATP1B1, OATP1B3, OATP2B1, and sodium-taurocholate co-transporting polypeptide (NTCP) on hepatic uptake of several OATP1B biomarkers, including coproporphyrin I (CPI), coproporphyrin I CPIII, and sulfate conjugates of bile acids: glycochenodeoxycholic acid sulfate (GCDCA-S), glycodeoxycholic acid sulfate (GDCA-S), and taurochenodeoxycholic acid sulfate (TCDCA-S). RAF values for OATP1B1, OATP1B3, OATP2B1, and NTCP were determined in cryopreserved human hepatocytes and transporter transfected cells using pitavastatin, cholecystokinin, resveratrol-3-O-β-D-glucuronide, and taurocholic acid (TCA) as reference compounds, respectively. OATP1B1-specific pitavastatin uptake in hepatocytes was measured in the absence and presence of 1 µM estropipate, whereas NTCP-specific TCA uptake was measured in the presence of 10 µM rifampin. Our studies suggested that CPI was a more selective biomarker for OATP1B1 than CPIII, whereas GCDCA-S and TCDCA-S were more selective to OATP1B3. OATP1B1 and OATP1B3 equally contributed to hepatic uptake of GDCA-S. The mechanistic static model, incorporating the fraction transported of CPI/III estimated by RAF and in vivo elimination data, predicted several perpetrator interactions with CPI/III. Overall, RAF method combined with pharmacogenomic and DDI studies is a useful tool to determine the selectivity of transporter biomarkers and facilitate the selection of appropriate biomarkers for DDI evaluation. SIGNIFICANCE STATEMENT: The authors developed a new relative activity factor (RAF) method to quantify the contribution of hepatic uptake transporters organic anion transporting polypeptide (OATP)1B1, OATP1B3, OATP2B1, and sodium taurocholate co-transporting polypeptide (NTCP) on several OATP1B biomarkers and evaluated their predictive value on drug-drug interactions (DDI). These studies suggest that the RAF method is a useful tool to determine the selectivity of transporter biomarkers. This method combined with pharmacogenomic and DDI studies will mechanistically facilitate the selection of appropriate biomarkers for DDI prediction.

Pharmacokinetic and Pharmacodynamic Drug-Drug Interactions: Research Methods and Applications

Because of the high research and development cost of new drugs, the long development process of new drugs, and the high failure rate at later stages, combining past drugs has gradually become a more economical and attractive alternative. However, the ensuing problem of drug-drug interactions (DDIs) urgently need to be solved, and combination has attracted a lot of attention from pharmaceutical researchers. At present, DDI is often evaluated and investigated from two perspectives: pharmacodynamics and pharmacokinetics. However, in some special cases, DDI cannot be accurately evaluated from a single perspective. Therefore, this review describes and compares the current DDI evaluation methods based on two aspects: pharmacokinetic interaction and pharmacodynamic interaction. The methods summarized in this paper mainly include probe drug cocktail methods, liver microsome and hepatocyte models, static models, physiologically based pharmacokinetic models, machine learning models, in vivo comparative efficacy studies, and in vitro static and dynamic tests. This review aims to serve as a useful guide for interested researchers to promote more scientific accuracy and clinical practical use of DDI studies.

Integrated Use of In Vitro and In Vivo Information for Comprehensive Prediction of Drug Interactions Due to Inhibition of Multiple CYP Isoenzymes

BACKGROUND

Mechanistic static pharmacokinetic (MSPK) models are simple, have fewer data requirements, and have broader applicability; however, they cannot use in vitro information and cannot distinguish the contributions of multiple cytochrome P450 (CYP) isoenzymes and the hepatic and intestinal first-pass effects appropriately. We aimed to establish a new MSPK analysis framework for the comprehensive prediction of drug interactions (DIs) to overcome these disadvantages.

METHODS

Drug interactions that occurred by inhibiting CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A in the liver and CYP3A in the intestine were simultaneously analyzed for 59 substrates and 35 inhibitors. As in vivo information, the observed changes in the area under the concentration-time curve (AUC) and elimination half-life (t1/2), hepatic availability, and urinary excretion ratio were used. As in vitro information, the fraction metabolized (fm) and the inhibition constant (Ki) were used. The contribution ratio (CR) and inhibition ratio (IR) for multiple clearance pathways and hypothetical volume (VHyp) were inferred using the Markov Chain Monte Carlo (MCMC) method.

RESULT

Using in vivo information from 239 combinations and in vitro 172 fm and 344 Ki values, changes in AUC, and t1/2 were estimated for all 2065 combinations, wherein the AUC was estimated to be more than doubled for 602 combinations. Intake-dependent selective intestinal CYP3A inhibition by grapefruit juice has been suggested. By separating the intestinal contributions, DIs after intravenous dosing were also appropriately inferred.

CONCLUSION

This framework would be a powerful tool for the reasonable management of various DIs based on all available in vitro and in vivo information.

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.

Cluster Gauss-Newton method analyses of PBPK model parameter combinations of coproporphyrin-I based on OATP1B-mediated rifampicin interaction studies

Coproporphyrin I (CP-I) is an endogenous biomarker supporting the prediction of drug-drug interactions (DDIs) involving hepatic organic anion transporting polypeptide 1B (OATP1B). We previously constructed a physiologically-based pharmacokinetic (PBPK) model for CP-I using clinical DDI data with an OATP1B inhibitor, rifampicin (RIF). In this study, PBPK model parameters for CP-I were estimated using the cluster Gauss-Newton method (CGNM), an algorithm used to find multiple approximate solutions for nonlinear least-squares problems. Eight unknown parameters including the hepatic overall intrinsic clearance (CL_int,all ), the rate of biosynthesis (v_syn ), and the OATP1B inhibition constant of RIF(K_i,u,OATP ) were estimated by fitting to the observed CP-I blood concentrations in two different clinical studies involving changing the RIF dose. Multiple parameter combinations were obtained by CGNM that could well capture the clinical data. Among those, CL_int,all , K_i,u,OATP , and v_syn were sensitive parameters. The obtained K_i,u,OATP for CP-I was 5.0- and 2.8-fold lower than that obtained for statins, confirming our previous findings describing substrate-dependent K_i,u,OATP values. In conclusion, CGNM analyses of PBPK model parameter combinations enables estimation of the three essential parameters for CP-I to capture the DDI profiles, even if the other parameters remain unidentified. The CGNM also clarified the importance of appropriate combinations of other unidentified parameters to enable capture of the CP-I concentration time course under the influence of RIF. The described CGNM approach may also support the construction of robust PBPK models for additional transporter biomarkers beyond CP-I.

Reimagining the Framework Supporting the Static Analysis of Transporter Drug Interaction Risk; Integrated Use of Biomarkers to Generate Pan‐Transporter Inhibition Signatures

Solute carrier (SLC) transporters present as the loci of important drug-drug interactions (DDIs). Therefore, sponsors generate in vitro half-maximal inhibitory concentration (IC₅₀ ) data and apply regulatory agency-guided "static" methods to assess DDI risk and the need for a formal clinical DDI study. Because such methods are conservative and high false-positive rates are likely (e.g., DDI study triggered when liver SLC R value ≥ 1.04 and renal SLC maximal unbound plasma (C_max,u )/IC₅₀ ratio ≥ 0.02), investigators have attempted to deploy plasma- and urine-based SLC biomarkers in phase I studies to de-risk DDI and obviate the need for drug probe-based studies. In this regard, it was possible to generate in-house in vitro SLC IC₅₀ data for various clinically (biomarker)-qualified perpetrator drugs, under standard assay conditions, and then estimate "% inhibition" for each SLC and relate it empirically to published clinical biomarker data (area under the plasma concentration vs. time curve (AUC) ratio (AUCR, AUC_inhibitor /AUC_reference ) and % decrease in renal clearance (ΔCL_renal )). After such a "calibration" exercise, it was determined that only compounds with high R values (> 1.5) and C_max,u /IC₅₀ ratios (> 0.5) are likely to significantly modulate liver (AUCR > 1.25) and renal (ΔCL_renal  > 25%) biomarkers and evoke DDI risk. The % inhibition approach supports integration of liver and renal SLC data and allows one to generate pan-SLC inhibition signatures for different test perpetrators (e.g., SLC % inhibition ranking). In turn, such signatures can guide the selection of the most appropriate individual (or combinations of) biomarkers for testing in phase I studies.

Utilizing Endogenous Biomarkers to Derisk Assessment of Transporter Mediated Drug-Drug Interactions: A Scientific Perspective

Comprehensive characterization of transporter mediated drug-drug interactions (DDIs) is important to formulate clinical management strategies and ensure the safe and effective use of concomitantly administered drugs. The potential of a drug to inhibit transporters is predicted by comparing the ratio of the relevant concentration (depending on the transporter) and the half maximum inhibitory concentration (IC₅₀ ) to a pre-defined "cut off" value. If the ratio is greater than the cut off value, modeling approaches such as Physiologically Based Pharmacokinetic (PBPK) Modeling or a clinical DDI trial may be recommended. Because false positive (in vitro data suggests the potential for a DDI, whereas no significant DDI is observed in vivo) and false negative (in vitro data does not suggest the potential for a DDI, whereas significant DDI is observed in vivo) outcomes have been observed, there is interest in exploring additional approaches to facilitate prediction of transporter mediated DDIs. The idea of assessing changes in the concentration of endogenous biomarkers (which are substrates of clinically relevant transporters) to gain insight on the potential for a drug to inhibit transporter activity has received widespread attention. This brief report describes how endogenous biomarkers may help to expand the DDI assessment toolkit, highlights some current knowledge gaps, and outlines a conceptual framework that may complement the current paradigm of predicting the potential for transporter mediated DDIs. This article is protected by copyright. All rights reserved.

Effect of Cyclosporin A and Impact of Dose Staggering on OATP1B1/1B3 Endogenous Substrates and Drug Probes for Assessing Clinical Drug Interactions

This study was designed to assess the quantitative performance of endogenous biomarkers for organic anion transporting polypeptide (OATP) 1B1/1B3‐mediated drug‐drug interactions (DDIs). Ten healthy volunteers orally received OATP1B1/1B3 probe cocktail (0.2 mg pitavastatin, 1 mg rosuvastatin, and 2 mg valsartan) and an oral dose of cyclosporin A (CysA, 20 mg and 75 mg) separated by a 1‐hour interval (20 mg (−1 hour), and 75 mg (−1 hour)). CysA 75 mg was also given with a 3‐hour interval (75 mg (−3 hours)) to examine the persistence of OATP1B1/1B3 inhibition. The area under the plasma concentration‐time curve ratios (AUCRs) were 1.63, 3.46, and 2.38 (pitavastatin), 1.39, 2.16, and 1.81 (rosuvastatin), and 1.42, 1.77, and 1.85 (valsartan), at 20 mg, 75 mg (−1 hour) and 75 mg (−3 hours) of CysA, respectively. CysA effect on OATP1B1/1B3 was unlikely to persist at the dose examined. Among 26 putative OATP1B1/1B3 biomarkers evaluated, AUCR and maximum concentration ratio (C_maxR) of CP‐I showed the highest Pearson’s correlation coefficient with CysA AUC (0.94 and 0.93, respectively). Correlation between AUCR of pitavastatin, and C_maxR or AUCR of CP‐I were consistent between this study and our previous study using rifampicin as an OATP1B1/1B3 inhibitor. Nonlinear regression analysis of AUCR⁻¹ of pitavastatin and CP‐I against CysA C_max yielded K_{i,OATP1B1/1B3,app} (109 ± 35 and 176 ± 42 nM, respectively), similar to the K_{i,OATP1B1/1B3} estimated by our physiologically‐based pharmacokinetic model analysis described previously (107 nM). The endogenous OATP1B1/1B3 biomarkers, particularly C_maxR and AUCR of CP‐I, corroborates OATP1B1/1B3 inhibition and yields valuable information that improve accurate DDI predictions in drug development, and enhance our understanding of interindividual variability in the magnitude of DDIs.

Quantitative prediction of OATP1B-mediated drug-drug interactions using endogenous biomarker coproporphyrin I

1. Evaluation of the organic anion transporting polypeptide (OATP) 1B-mediated drug-drug interaction (DDI) potential is important for drug development. The focus of this study was coproporphyrin I (CP-I), an endogenous OATP1B biomarker.2. We investigated a new approach to OATP1B-mediated DDI prediction based on the mechanistic static pharmacokinetics (MSPK) model.3. The ratio of the area under the plasma concentration-time curve (AUCR) with and without co-administration of rifampicin (a typical OATP1B inhibitor) was found for CP-I and OATP1B substrate, respectively, and was then used to derive the correlation curve equation. The AUCR with and without co-administration of another OATP1B inhibitor than rifampicin was then predicted for the OATP1B substrates by substituting the AUCR of CP-I in the correlation curve equation to verify the predictability of the AUCR of the OATP1B substrates.4. The derived correlation curve equation between CP-I and the OATP1B substrates of the AUCRs with and without co-administration of rifampicin matched the observed AUCRs well. Regarding pitavastatin, rosuvastatin and pravastatin, 92.9% of the predicted AUCR values were within a two-fold range of the observed values, indicating that this approach may be a good way to quantitatively predict DDI potential.

Renal Transporter-Mediated Drug-Biomarker Interactions of the Endogenous Substrates Creatinine and N1 -Methylnicotinamide: A PBPK Modeling Approach

Endogenous biomarkers for transporter-mediated drug-drug interaction (DDI) predictions represent a promising approach to facilitate and improve conventional DDI investigations in clinical studies. This approach requires high sensitivity and specificity of biomarkers for the targets of interest (e.g., transport proteins), as well as rigorous characterization of their kinetics, which can be accomplished utilizing physiologically-based pharmacokinetic (PBPK) modeling. Therefore, the objective of this study was to develop PBPK models of the endogenous organic cation transporter (OCT)2 and multidrug and toxin extrusion protein (MATE)1 substrates creatinine and N¹ -methylnicotinamide (NMN). Additionally, this study aimed to predict kinetic changes of the biomarkers during administration of the OCT2 and MATE1 perpetrator drugs trimethoprim, pyrimethamine, and cimetidine. Whole-body PBPK models of creatinine and NMN were developed utilizing studies investigating creatinine or NMN exogenous administration and endogenous synthesis. The newly developed models accurately describe and predict observed plasma concentration-time profiles and urinary excretion of both biomarkers. Subsequently, models were coupled to the previously built and evaluated perpetrator models of trimethoprim, pyrimethamine, and cimetidine for interaction predictions. Increased creatinine plasma concentrations and decreased urinary excretion during the drug-biomarker interactions with trimethoprim, pyrimethamine, and cimetidine were well-described. An additional inhibition of NMN synthesis by trimethoprim and pyrimethamine was hypothesized, improving NMN plasma and urine interaction predictions. To summarize, whole-body PBPK models of creatinine and NMN were built and evaluated to better assess creatinine and NMN kinetics while uncovering knowledge gaps for future research. The models can support investigations of renal transporter-mediated DDIs during drug development.

Physiologically-based pharmacokinetic model-based translation of OATP1B-mediated drug-drug interactions from coproporphyrin I to probe drugs

The accurate prediction of OATP1B-mediated drug-drug interactions (DDIs) is challenging for drug development. Here, we report physiologically-based pharmacokinetic (PBPK) model analysis for clinical DDI data generated in heathy subjects who received oral doses of cyclosporin A (CysA; 20 and 75 mg) as an OATP1B inhibitor, and the probe drugs (pitavastatin, rosuvastatin and valsartan). PBPK models of CysA and probe compounds were combined assuming inhibition of hepatic uptake of endogenous coproporphyrin I (CP-I) by CysA. In vivo K_i of unbound CysA for OATP1B (K_i,OATP1B ), and the overall intrinsic hepatic clearance per body weight of CP-I (CL_int,all,unit ) were optimized to account for the CP-I data (K_i,OATP1B , 0.657 ± 0.048 nM; CL_int,all,unit , 57.0 ± 6.3 L/h/kg). DDI simulation using K_i,OATP1B reproduced the dose-dependent effect of CysA (20 and 75 mg) and the dosing interval (1 h and 3 h) on the time profiles of blood concentrations of pitavastatin and rosuvastatin, but DDI simulation using in vitro K_i,OATP1B failed. The Cluster Gauss-Newton method was used to conduct parameter optimization using 1,000 initial parameter sets for the seven pharmacokinetic parameters of CP-I (β, CL_int,all , F_a F_g , R_dif , f_bile , f_syn , and v_syn ), and K_i,OATP1B , and K_i,MRP2 of CysA. Based on the accepted 498 parameter sets, the range of CL_int,all and K_i,OATP1B was narrowed, with coefficients of variation (CVs) of 9.3% and 11.1%, respectively, indicating that these parameters were practically identifiable. These results suggest that PBPK model analysis of CP-I is a promising translational approach to predict OATP1B-mediated DDIs in drug development.

Regulation of Drug Transport Proteins-From Mechanisms to Clinical Impact: A White Paper on Behalf of the International Transporter Consortium

Membrane transport proteins are involved in the absorption, disposition, efficacy, and/or toxicity of many drugs. Numerous mechanisms (e.g., nuclear receptors, epigenetic gene regulation, microRNAs, alternative splicing, post‐translational modifications, and trafficking) regulate transport protein levels, localization, and function. Various factors associated with disease, medications, and dietary constituents, for example, may alter the regulation and activity of transport proteins in the intestine, liver, kidneys, brain, lungs, placenta, and other important sites, such as tumor tissue. This white paper reviews key mechanisms and regulatory factors that alter the function of clinically relevant transport proteins involved in drug disposition. Current considerations with in vitro and in vivo models that are used to investigate transporter regulation are discussed, including strengths, limitations, and the inherent challenges in predicting the impact of changes due to regulation of one transporter on compensatory pathways and overall drug disposition. In addition, translation and scaling of in vitro observations to in vivo outcomes are considered. The importance of incorporating altered transporter regulation in modeling and simulation approaches to predict the clinical impact on drug disposition is also discussed. Regulation of transporters is highly complex and, therefore, identification of knowledge gaps will aid in directing future research to expand our understanding of clinically relevant molecular mechanisms of transporter regulation. This information is critical to the development of tools and approaches to improve therapeutic outcomes by predicting more accurately the impact of regulation‐mediated changes in transporter function on drug disposition and response.

Effect of a Ketohexokinase Inhibitor (PF-06835919) on In Vivo OATP1B Activity: Integrative Risk Assessment Using Endogenous Biomarker and a Probe Drug

PF-06835919 is a first-in-class ketohexokinase inhibitor (KHKi), recently under development for the treatment of metabolic and fatty liver diseases, which inhibited organic anion transporting polypeptide (OATP)1B1 in vitro and presented drug-drug interaction (DDI) risk. This study aims to investigate the dose-dependent effect of KHKi on OATP1B in vivo activity. We performed an open-label study comparing pharmacokinetics of atorvastatin (OATP1B probe) dosed alone (20 mg single dose) and coadministered with two dose strengths of KHKi (50 and 280 mg once daily) in 12 healthy participants. Additionally, changes in exposure of coproporphyrin-I (CP-I), an endogenous biomarker for OATP1B, were assessed in the atorvastatin study (1.12-fold and 1.49-fold increase in area under the plasma concentration-time profile (AUC) with once-daily 50 and 280 mg, respectively), and a separate single oral dose study of KHKi alone (100-600 mg, n = 6 healthy participants; up to a 1.80-fold increase in AUC). Geometric mean ratios (90% confidence interval) of atorvastatin (area under the plasma concentration - time profile from time 0 extrapolated to infinite time) AUC_inf following 50 and 280 mg KHKi were 1.14 (1.00-1.30) and 1.54 (1.37-1.74), respectively. Physiologically-based pharmacokinetic modeling of CP-I plasma exposure following a single dose of KHKi predicted in vivo OATP1B inhibition from about 13% to 70% over the 100 to 600 mg dose range, while using the in vitro inhibition potency (1.9 µM). Model-based analysis correctly predicted "no-effect" (AUC ratio < 1.25) at the low dose range and "weak" effect (AUC ratio < 2) on atorvastatin pharmacokinetics at the high dose range of KHKi. This study exemplified the utility of biomarker-informed model-based approach in discerning even small effects on OATP1B activity in vivo, and to project DDI risk at the clinically relevant doses.

The utility of endogenous glycochenodeoxycholate-3-sulfate and 4β-hydroxycholesterol to evaluate the hepatic disposition of atorvastatin in rats

The liver is an important organ for drugs disposition, and thus how to accurately evaluate hepatic clearance is essential for proper drug dosing. However, there are many limitations in drug dosage adjustment based on liver function and pharmacogenomic testing. In this study, we evaluated the ability of endogenous glycochenodeoxycholate-3-sulfate (GCDCA-S) and 4β-hydroxycholesterol (4β-HC) plasma levels to evaluate organic anion-transporting polypeptide (Oatps)-mediated hepatic uptake and Cyp3a-meidated metabolism of atorvastatin (ATV) in rats. The concentration of ATV and its metabolites, 2-OH ATV and 4-OH ATV, was markedly increased after a single injection of rifampicin (RIF), an inhibitor of Oatps. Concurrently, plasma GCDCA-S levels were also elevated. After a single injection of the Cyp3a inhibitor ketoconazole (KTZ), plasma ATV concentrations were significantly increased and 2-OH ATV concentrations were decreased, consistent with the metabolism of ATV by Cyp3a. However, plasma 4β-HC was not affected by KTZ treatment despite it being a Cyp3a metabolite of cholesterol. After repeated oral administration of RIF, plasma concentrations of ATV, 2-OH ATV and 4-OH ATV were markedly increased and the hepatic uptake ratio of ATV and GCDCA-S was decreased. KTZ did not affect plasma concentrations of ATV, 2-OH ATV and 4-OH ATV, but significantly decreased the metabolic ratio of total and 4-OH ATV. However, the plasma level and hepatic metabolism of 4β-HC were not changed by KTZ. The inhibition of hepatic uptake of GCDCA-S by RIF was fully reversed after a 7-d washout of RIF. Plasma concentration and hepatic uptake ratio of GCDCA-S were correlated with the plasma level and hepatic uptake of ATV in rats with ANIT-induced liver injury, respectively. These results demonstrate that plasma GCDCA-S is a sensitive probe for the assessment of Oatps-mediated hepatic uptake of ATV. However, Cyp3a-mediated metabolism of ATV was not predicted by plasma 4β-HC levels in rats.

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

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

Recent Advances in Drug Transporter Sciences: Highlights From the Year 2020

Drug Metabolism Reviews has an impressive track record of providing scientific reviews in the area of xenobiotic biotransformation over 47 years. It has consistently proved to be resourceful to many scientists from pharmaceutical industry, academia, regulatory agencies working in diverse areas including enzymology, pharmacology, pharmacokinetics and toxicology. Over the last 5 years Drug metabolism Reviews has annually published an industry commentary aimed to highlight novel insights and approaches that have made significant impacts on the field of biotransformation (led by Cyrus Khojasteh). We hope to continue this tradition by providing an overview of advances made in the field of drug transporters during 2020. The field of drug transporters is rapidly evolving as they play an essential role in drug absorption, distribution, clearance and elimination. In this review we have selected outstanding drug transporter articles that have significantly contributed to moving forward the field of transporter science with respect to translation and improved understanding of diverse aspects including uptake clearance, clinical biomarkers, induction, proteomics, emerging transporters and tissue targeting.The theme of this review consists of synopsis that summarizes each article followed by our commentary. The objective of this work is not to provide a comprehensive review but rather exemplify novel insights and state-of-the-art highlights of recent research that have advanced our understanding of drug transporters in drug disposition. We are hopeful that this effort will prove useful to the scientific community and as such request feedback, and further extend an invitation to anyone interested in contributing to future reviews.

Factors Influencing Plasma Coproporphyrin-I Concentration as Biomarker of OATP1B Activity in Patients With Rheumatoid Arthritis

Organic anion transporting polypeptides (OATPs) 1B are drug transporters mainly expressed in the sinusoidal membrane. In previous reports, genetic factor, 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF), which is one of the uremic toxins, inflammatory cytokines such as tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) decreased OATP1B1 activity in vitro, but in vivo effects of these factors have not been elucidated. Plasma coproporphyrin-I (CP-I) is spotlighted as a highly accurate endogenous substrate of OATP1B. This study focused on patients with rheumatoid arthritis (RA) and evaluated the influence of several factors comprising gene polymorphisms, uremic toxins, and inflammatory cytokines on OATP1B activity using plasma CP-I concentration. Thirty-seven outpatients with RA who satisfied the selection criteria were analyzed at the time of recruitment (baseline) and at the next visit. OATP1B1*15 carriers tended to have higher CP-I concentration compared with noncarriers. Plasma CP-I correlated positively with CMPF concentration, but did not correlate with IL-6 or TNF-α concentration. Multiple logistic regression analysis by stepwise selection identified plasma CMPF concentration and OATP1B1*15 allele as significant factors independently affecting plasma CP-I concentration at baseline and at the next visit, respectively. In conclusion, the present results suggest that inflammatory cytokines do not have clinically significant effects on OATP1B activity, whereas the effects of genetic polymorphisms and uremic toxins should be considered.

Application of a PBPK model to elucidate the changes of systemic and liver exposures for rosuvastatin, carotegrast, and bromfenac followed by OATP inhibition in monkeys

The impact of organic anion‐transporting polypeptide (OATP) inhibition on systemic and liver exposures of three OATP substrates was investigated in cynomolgus monkeys. A monkey physiologically‐based pharmacokinetic (PBPK) model was constructed to describe the exposure changes followed by OATP functional attenuation. Rosuvastatin, bromfenac, and carotegrast were administered as a single intravenous cassette dose (0.5 mg/kg each) in monkeys with and without predosing with rifampin (RIF; 20 mg/kg) orally. The plasma exposure of rosuvastatin, bromfenac, carotegrast, and OATP biomarkers, coproporphyrin I (CP‐I) and CP‐III were increased 2.3, 2.1, 9.1, 5.4, and 8.8‐fold, respectively, when compared to the vehicle group. The liver to plasma ratios of rosuvastatin and bromfenac were reduced but the liver concentration of the drugs remained unchanged by RIF treatment. The liver concentrations of carotegrast, CP‐I, and CP‐III were unchanged at 1 h but increased at 6 h in the RIF‐treated group. The passive permeability, active uptake, and biliary excretion were characterized in suspended and sandwich‐cultured monkey hepatocytes and then incorporated into the monkey PBPK model. As demonstrated by the PBPK model, the plasma exposure is increased through OATP inhibition while liver exposure is maintained by passive permeability driven from an elevated plasma level. Liver exposure is sensitive to the changes of metabolism and biliary clearances. The model further suggested the involvement of additional mechanisms for hepatic uptakes of rosuvastatin and bromfenac, and of the inhibition of biliary excretion for carotegrast, CP‐I, and CP‐III by RIF. Collectively, impaired OATP function would not reduce the liver exposure of its substrates in monkeys.

Population pharmacokinetic modeling and simulation to support qualification of pyridoxic acid as endogenous biomarker of OAT1/3 renal transporters

Renal clearance of many drugs is mediated by renal organic anion transporters OAT1/3 and inhibition of these transporters may lead to drug‐drug interactions (DDIs). Pyridoxic acid (PDA) and homovanillic acid (HVA) were indicated as potential biomarkers of OAT1/3. The objective of this study was to develop a population pharmacokinetic model for PDA and HVA to support biomarker qualification. Simultaneous fitting of biomarker plasma and urine data in the presence and absence of potent OAT1/3 inhibitor (probenecid, 500 mg every 6 h) was performed. The impact of study design (multiple vs. single dose of OAT1/3 inhibitor) and ability to detect interactions in the presence of weak/moderate OAT1/3 inhibitors was investigated, together with corresponding power calculations. The population models developed successfully described biomarker baseline and PDA/HVA OAT1/3‐mediated interaction data. No prominent effect of circadian rhythm on PDA and HVA individual baseline levels was evident. Renal elimination contributed greater than 80% to total clearance of both endogenous biomarkers investigated. Estimated probenecid unbound in vivo OAT inhibitory constant was up to 6.4‐fold lower than in vitro values obtained with PDA as a probe. The PDA model was successfully verified against independent literature reported datasets. No significant difference in power of DDI detection was found between multiple and single dose study design when using the same total daily dose of 2000 mg probenecid. Model‐based simulations and power calculations confirmed sensitivity and robustness of plasma PDA data to identify weak, moderate, and strong OAT1/3 inhibitors in an adequately powered clinical study to support optimal design of prospective clinical OAT1/3 interaction studies.

OATP2B1 - The underrated member of the organic anion transporting polypeptide family of drug transporters?

The organic anion transporting polypeptide 2B1 (OATP2B1) was one of the first cloned members of the SLCO family. However, its physiological and pharmacological role is still poorly understood, and object of a current debate on the transporter's relevance. Within this commentary, we summarize the data currently available on the transporter's expression and its substrates and highlight the strength and difficulties of the methods that have been applied to gather these data. The conclusion drawn from these findings was that OATP2B1 due to its intestinal expression is most likely involved in oral drug absorption of its substrate and therefore prone for interactions. This has been tested in in vivo drug interaction and/or pharmacogenetic studies. While some of these support the notion of OATP2B1 being of relevance in drug absorption, the pharmacogenetic findings are rather inconclusive. We will explain our thoughts why OATP2B1 may not influence the general systemic pharmacokinetic of certain substrates, but possibly local distribution processes, like the transfer across the blood-brain-barrier. Besides the pharmacokinetic aspects, there are data on endogenous molecules like coproporphyrins and sulfated steroids. Therefore, we will also highlight possible physiological roles of OATP2B1, which are driven by its expression pattern in the tubular cells of the kidney as well as its expression in the blood brain barrier. Finally we also deal with the advantages and disadvantages in the use of animal models to decipher the role of OATP2B1 in pharmacokinetics of its substrates and beyond.

Relationship of hemoglobin level and plasma coproporphyrin-I concentrations as an endogenous probe for phenotyping OATP1B

Plasma coproporphyrin‐I (CP‐I) concentration is used as a sensitive and selective endogenous probe for phenotyping organic anion transporting polypeptides 1B (OATP1B) activity in many studies. CP‐I is produced in the process of heme synthesis, but the relationship between plasma CP‐I concentrations and heme synthesis activity is unknown. In this study, we evaluated the relationship between plasma CP‐I concentration and hemoglobin level as a biomarker of heme synthesis activity. The data of 391 subjects selected from the Japanese general population were analyzed. One hundred twenty‐six participants had OATP1B1*15 allele, 11 of whom were homozygous (OATP1B1*15/*15). Multiple regression analysis identified hemoglobin level as an independent variable associated with plasma CP‐I concentration (p < 0.0001). A significant positive correlation was observed between hemoglobin level and plasma CP‐I concentration in participants without OATP1B1*15 allele (n = 265; r_s = 0.35, p < 0.0001) and with OATP1B1*15 allele (n = 126; r_s =0.27, p = 0.0022). However, Kruskal–Wallis test showed no large difference in Kruskal–Wallis statistics between the distribution of plasma CP‐I concentrations and that of ratio of plasma CP‐I to hemoglobin among six OATP1B1 polymorphism groups. These findings suggest that the hemoglobin level seems to reflect biosynthesis of CP‐I. However, correction by hemoglobin level is not required when using basal plasma CP‐I concentration for phenotyping OATP1B activity.

PBPK Model of Coproporphyrin I: Evaluation of the Impact of SLCO1B1 Genotype, Ethnicity, and Sex on its Inter-Individual Variability

Coproporphyrin I (CPI) is an endogenous biomarker of OATP1B activity and associated drug-drug interactions. In this study, a minimal physiologically-based pharmacokinetic model was developed to investigate the impact of OATP1B1 genotype (c.521T>C), ethnicity, and sex on CPI pharmacokinetics and interindividual variability in its baseline. The model implemented mechanistic descriptions of CPI hepatic transport between liver blood and liver tissue and renal excretion. Key model parameters (e.g., endogenous CPI synthesis rate, and CPI hepatic uptake clearance) were estimated by fitting the model simultaneously to three independent CPI clinical datasets (plasma and urine data) obtained from white (n = 16, men and women) and Asian-Indian (n = 26, all men) subjects, with c.521 variants (TT, TC, and CC). The optimized CPI model successfully described the observed data using c.521T>C genotype, ethnicity, and sex as covariates. CPI hepatic active was 79% lower in 521CC relative to the wild type and 42% lower in Asian-Indians relative to white subjects, whereas CPI synthesis was 23% higher in male relative to female subjects. Parameter sensitivity analysis showed marginal impact of the assumption of CPI synthesis site (blood or liver), resulting in comparable recovery of plasma and urine CPI data. Lower magnitude of CPI-drug interaction was simulated in 521CC subjects, suggesting the risk of underestimation of CPI-drug interaction without prior OATP1B1 genotyping. The CPI model incorporates key covariates contributing to interindividual variability in its baseline and highlights the utility of the CPI modeling to facilitate the design of prospective clinical studies to maximize the sensitivity of this biomarker.

Endogenous Coproporphyrin I and III are Altered in Multidrug Resistance-Associated Protein 2-Deficient (TR-) Rats

Recent studies have focused on coproporphyrin (CP)-I and CP-III (CPs) as endogenous biomarkers for organic anion transporting polypeptides (OATPs). Previous data showed that CPs are also substrates of multidrug resistance-associated protein (MRP/Mrp) 2 and 3. This study was designed to examine the impact of loss of Mrp2 function on the routes of excretion of endogenous CPs in wild-type (WT) Wistar compared to Mrp2-deficient TR- rats. To exclude possible confounding effects of rat Oatps, the transport of CPs was investigated in Oatp-overexpressing HeLa cells. Results indicated that CPs are substrates of rodent Oatp1b2, and that CP-III is a substrate of Oatp2b1. Quantitative targeted absolute proteomic (QTAP) analysis revealed no differences in Oatps, but an expected significant increase in Mrp3 protein levels in TR- compared to WT rat livers. CP-I and CP-III concentrations measured by LC-MS/MS were elevated in TR- compared to WT rat liver, while CP-I and CP-III estimated biliary clearance was decreased 75- and 840-fold in TR- compared to WT rats, respectively. CP-III concentrations were decreased 14-fold in the feces of TR- compared to WT rats, but differences in CP-I were not significant. In summary, the disposition of CPs was markedly altered by loss of Mrp2 and increased Mrp3 function as measured in TR- rats.

Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling to Support Waivers of In Vivo Clinical Studies: Current Status, Challenges, and Opportunities

Physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling has been extensively applied to quantitatively translate in vitro data, predict the in vivo performance, and ultimately support waivers of in vivo clinical studies. In the area of biopharmaceutics and within the context of model-informed drug discovery and development (MID3), there is a rapidly growing interest in applying verified and validated mechanistic PBPK models to waive in vivo clinical studies. However, the regulatory acceptance of PBPK analyses for biopharmaceutics and oral drug absorption applications, which is also referred to variously as "PBPK absorption modeling" [Zhang et al. CPT: Pharmacometrics Syst. Pharmacol. 2017, 6, 492], "physiologically based absorption modeling", or "physiologically based biopharmaceutics modeling" (PBBM), remains rather low [Kesisoglou et al. J. Pharm. Sci. 2016, 105, 2723] [Heimbach et al. AAPS J. 2019, 21, 29]. Despite considerable progress in the understanding of gastrointestinal (GI) physiology, in vitro biopharmaceutic and in silico tools, PBPK models for oral absorption often suffer from an incomplete understanding of the physiology, overparameterization, and insufficient model validation and/or platform verification, all of which can represent limitations to their translatability and predictive performance. The complex interactions of drug substances and (bioenabling) formulations with the highly dynamic and heterogeneous environment of the GI tract in different age, ethnic, and genetic groups as well as disease states have not been yet fully elucidated, and they deserve further research. Along with advancements in the understanding of GI physiology and refinement of current or development of fully mechanistic in silico tools, we strongly believe that harmonization, interdisciplinary interaction, and enhancement of the translational link between in vitro, in silico, and in vivo will determine the future of PBBM. This Perspective provides an overview of the current status of PBBM, reflects on challenges and knowledge gaps, and discusses future opportunities around PBPK/PD models for oral absorption of small and large molecules to waive in vivo clinical studies.

Assessing Transporter-Mediated Natural Product-Drug Interactions Via In vitro-In Vivo Extrapolation: Clinical Evaluation With a Probe Cocktail

The botanical natural product goldenseal can precipitate clinical drug interactions by inhibiting cytochrome P450 (CYP) 3A and CYP2D6. Besides P-glycoprotein, effects of goldenseal on other clinically relevant transporters remain unknown. Established transporter-expressing cell systems were used to determine the inhibitory effects of a goldenseal extract, standardized to the major alkaloid berberine, on transporter activity. Using recommended basic models, the extract was predicted to inhibit the efflux transporter BCRP and uptake transporters OATP1B1/3. Using a cocktail approach, effects of the goldenseal product on BCRP, OATP1B1/3, OATs, OCTs, MATEs, and CYP3A were next evaluated in 16 healthy volunteers. As expected, goldenseal increased the area under the plasma concentration-time curve (AUC_0-inf ) of midazolam (CYP3A; positive control), with a geometric mean ratio (GMR) (90% confidence interval (CI)) of 1.43 (1.35-1.53). However, goldenseal had no effects on the pharmacokinetics of rosuvastatin (BCRP and OATP1B1/3) and furosemide (OAT1/3); decreased metformin (OCT1/2, MATE1/2-K) AUC_0-inf (GMR, 0.77 (0.71-0.83)); and had no effect on metformin half-life and renal clearance. Results indicated that goldenseal altered intestinal permeability, transport, and/or other processes involved in metformin absorption, which may have unfavorable effects on glucose control. Inconsistencies between model predictions and pharmacokinetic outcomes prompt further refinement of current basic models to include differential transporter expression in relevant organs and intestinal degradation/metabolism of the precipitant(s). Such refinement should improve in vitro-in vivo prediction accuracy, contributing to a standard approach for studying transporter-mediated natural product-drug interactions.

Improved Prediction of the Drug-Drug Interactions of Pemafibrate Caused by Cyclosporine A and Rifampicin via PBPK Modeling: Consideration of the Albumin-Mediated Hepatic Uptake of Pemafibrate and Inhibition Constants With Preincubation Against OATP1B

Pemafibrate (PMF) is highly albumin-bound (>99.8%) and a substrate for hepatic uptake transporters (OATP1B) and CYP enzymes. Here, we developed a PBPK model of PMF to capture drug-drug interactions (DDI) incurred by cyclosporine (CsA) and rifampicin (RIF), the two OATP1B inhibitors. Initial PBPK modeling of PMF utilized in vitro hepatic uptake clearance (PS_inf) obtained in the absence of albumin, but failed in capturing the blood PMF pharmacokinetic (PK) profiles. Based on the results that in vitro PS_inf of unbound PMF was enhanced in the presence of albumin, we applied the albumin-facilitated dissociation model and the resulting PS_inf parameters improved the prediction of the blood PMF PK profiles. In refining our PBPK model toward improved prediction of the observed DDI data (PMF co-administered with single dosing of CsA or RIF; PMF following multiple RIF dosing), we adjusted the previously obtained in vivo OATP1B inhibition constants (K_i,OATP1B) of CsA or RIF for pitavastatin by correcting for substrate-dependency. We also incorporated the induction of OATP1B and CYP enzymes after multiple RIF dosing. Sensitivity analysis informed that the higher gastrointestinal absorption rate constant could further improve capturing the observed DDI data, suggesting the possible inhibition of intestinal ABC transporter(s) by CsA or RIF.

Substantially Increased Plasma Coproporphyrin-I Concentrations Associated With OATP1B1*15 Allele in Japanese General Population

Coproporphyrin-I (CP-I) in plasma is a sensitive and specific endogenous probe for phenotyping organic anion transporting polypeptides 1B (OATP1B, encoded by SLCO1B). A few small-scale studies suggested that plasma CP-I concentration is affected by OATP1B1 polymorphism, but detailed studies are lacking. In this large-scale study, we measured plasma CP-I concentrations in 391 subjects from the Japanese general population, and evaluated the relationship between plasma CP-I concentrations and OATP1B1 polymorphisms to further assess the utility of plasma CP-I concentrations as an endogenous OATP1B probe. Plasma CP-I concentrations were 0.45 ± 0.12, 0.47 ± 0.16, 0.47 ± 0.20, 0.50 ± 0.15, 0.54 ± 0.14, and 0.74 ± 0.31 ng/mL in participants with OATP1B1*1b/*1b (n = 103), *1a/*1b (n = 122), *1a/*1a (n = 40), *1b/*15 (n = 74), *1a/*15 (n = 41), and *15/*15 (n = 11), respectively, showing an ascending rank order with significant difference (P < 0.0001). Post hoc analysis revealed significant increases in plasma CP-I concentration in OATP1B1*1b/*15 (P = 0.036), *1a/*15 (P = 0.0005), and *15/*15 (P = 0.0003) groups compared with the OATP1B1*1b/*1b group. There was no significant difference among OATP1B genotypes in plasma concentration of 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid, a uremic toxin reported to decrease OATP1B activity in vivo. These findings confirm the utility of plasma CP-I concentrations as an endogenous biomarker for phenotyping of OATP1B activity. Plasma CP-I concentration is potentially useful for the study of drug-drug interactions via OATP1B or individual dose adjustment of OATP1B substrates.

Current progress in identifying endogenous biomarker candidates for drug transporter phenotyping and their potential application to drug development

Drug transporters play important roles in the elimination of various compounds from the blood. Genetic variation and drug-drug interactions underlie the pharmacokinetic differences for the substrates of drug transporters. Some endogenous substrates of drug transporters have emerged as biomarkers to assess differences in drug transporter activity-not only in animals, but also in humans. Metabolomic analysis is a promising approach for identifying such endogenous substrates through their metabolites. The appropriateness of metabolites is supported by studies in vitro and in vivo, both in animals and through pharmacogenomic or drug-drug interaction studies in humans. This review summarizes current progress in identifying such endogenous biomarkers and applying them to drug transporter phenotyping.

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

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

Alteration in the Plasma Concentrations of Endogenous Organic Anion-Transporting Polypeptide 1B Biomarkers in Patients with Non-Small Cell Lung Cancer Treated with Paclitaxel

Paclitaxel has been considered to cause OATP1B-mediated drug-drug interactions at therapeutic doses; however, its clinical relevance has not been demonstrated. This study aimed to elucidate in vivo inhibition potency of paclitaxel against OATP1B1 and OATP1B3 using endogenous OATP1B biomarkers. Paclitaxel is an inhibitor of OATP1B1 and OATP1B3, with K_i of 0.579 ± 0.107 and 5.29 ± 3.87 μM, respectively. Preincubation potentiated its inhibitory effect on both OATP1B1 and OATP1B3, with K_i of 0.154 ± 0.031 and 0.624 ± 0.183 μM, respectively. Ten patients with non-small cell lung cancer who received 200 mg/m² of paclitaxel by a 3-hour infusion were recruited. Plasma concentrations of 10 endogenous OATP1B biomarkers-namely, coproporphyrin I, coproporphyrin III, glycochenodeoxycholate-3-sulfate, glycochenodeoxycholate-3-glucuronide, glycodeoxycholate-3-sulfate, glycodeoxycholate-3-glucuronide, lithocholate-3-sulfate, glycolithocholate-3-sulfate, taurolithocholate-3-sulfate, and chenodeoxycholate-24-glucuronide-were determined in the patients with non-small cell lung cancer on the day before paclitaxel administration and after the end of paclitaxel infusion for 7 hours. Paclitaxel increased the area under the plasma concentration-time curve (AUC) of the endogenous biomarkers 2- to 4-fold, although a few patients did not show any increment in the AUC ratios of lithocholate-3-sulfate, glycolithocholate-3-sulfate, and taurolithocholate-3-sulfate. Therapeutic doses of paclitaxel for the treatment of non-small cell lung cancer (200 mg/m²) will cause significant OATP1B1 inhibition during and at the end of the infusion. This is the first demonstration that endogenous OATP1B biomarkers could serve as surrogate biomarkers in patients. SIGNIFICANCE STATEMENT: Endogenous biomarkers can address practical and ethical issues in elucidating transporter-mediated drug-drug interaction (DDI) risks of anticancer drugs clinically. We could elucidate a significant increment of the plasma concentrations of endogenous OATP1B biomarkers after a 3-hour infusion (200 mg/m²) of paclitaxel, a time-dependent inhibitor of OATP1B, in patients with non-small cell lung cancer. The endogenous OATP1B biomarkers are useful to assess the possibility of OATP1B-mediated DDIs in patients and help in appropriately designing a dosing schedule to avoid the DDIs.

Theoretical consideration of the properties of intestinal flow models on route-dependent drug removal: Segregated Flow (SFM) vs. Traditional (TM)

The intestine is endowed with a plethora of enzymes and transporters and regulates the flow of substrate to the liver. Physiologically-based pharmacokinetic models have surfaced to describe intestinal removal. The traditional model (TM) describes the intestinal flow as a whole perfusing the entire tissue that contains the intestinal transporters and enzymes. The segregated flow model (SFM) describes that only a fraction (f_Q  < 0.2) of the intestinal blood flow perfuses the enterocyte region where the intestinal enzymes and transporters are housed, rendering a lower drug distribution/intestinal clearance when drug enters via the circulation than from the gut lumen. As shown by simulations, a higher intestinal clearance and extraction ratio (E_I,iv ) exists for the TM than for SFM after iv dosing. By contrast, the E_I,po after po dosing is higher for the SFM, due to the smaller volume of distribution for the enterocyte region and a lower flow rate that result in increased mean residence time and higher drug extraction. Under MBI (mechanism-based inhibition), the AUC_R,po after oral bolus is the highest for drug when inhibitor is given orally, with SFM > TM. Competitive inhibition of intestinal enzymes leads to higher liver metabolism; again, when both drug and inhibitor are given orally, changes in the SFM > TM. However, less definitive patterns result with inhibition of both intestinal and liver enzymes. In conclusion, differences exist for E_I and drug-drug interaction (DDI) between the TM and SFM. The fractional intestinal blood flow (f_Q ) is a key factor affecting different extents of intestinal/liver metabolism of the drug after oral as well as intravenous administration.

Recent progress in in vivo phenotyping technologies for better prediction of transporter-mediated drug-drug interactions

Clinical reports on transporter-mediated drug-drug interactions (TP-DDIs) have rapidly accumulated and regulatory guidance/guidelines recommend that sponsors consider performing quantitative prediction of TP-DDI risks in the process of drug development. In vitro experiments for characterizing the function of drug transporters have been established and various parameters such as the inhibition constant (K_i) of drugs and the intrinsic uptake/efflux clearance for a certain transporter can be obtained. However, many reports have indicated large discrepancies between the parameters estimated from in vitro experiments and those rationally explaining drug pharmacokinetics. Thus, it is essential to evaluate directly the function of each transporter isoform in vivo in humans. At present, several transporter substrate drugs and endogenous compounds have been recognized as probe substrates for a specific transporter and transporter function was evaluated by monitoring the plasma and urine concentration of those probes; however, few compounds specifically transported via a single transporter isoform have been found. For monitoring the intraorgan concentration of drugs, positron emission tomography can be a powerful tool and clinical examples for quantification of in vivo transporter function have been published. In this review, novel methodologies for in vivo phenotyping of transporter function are summarized.

Dose-Dependent Inhibition of OATP1B by Rifampicin in Healthy Volunteers: Comprehensive Evaluation of Candidate Biomarkers and OATP1B Probe Drugs

To address the most appropriate endogenous biomarker for drug–drug interaction risk assessment, eight healthy subjects received an organic anion transporting polypeptide 1B (OATP1B) inhibitor (rifampicin, 150, 300, and 600 mg), and a probe drug cocktail (atorvastatin, pitavastatin, rosuvastatin, and valsartan). In addition to coproporphyrin I, a widely studied OATP1B biomarker, we identified at least 4 out of 28 compounds (direct bilirubin, glycochenodeoxycholate‐3‐glucuronide, glycochenodeoxycholate‐3‐sulfate, and hexadecanedioate) that presented good sensitivity and dynamic range in terms of the rifampicin dose‐dependent change in area under the plasma concentration‐time curve ratio (AUCR). Their suitability as OATP1B biomarkers was also supported by the good correlation of AUC_0‐24h between the endogenous compounds and the probe drugs, and by nonlinear regression analysis (AUCR⁻¹ vs. rifampicin plasma C_max (maximum total concentration in plasma)) to yield an estimate of the inhibition constant of rifampicin. These endogenous substrates can complement existing OATP1B‐mediated drug–drug interaction risk assessment approaches based on agency guidelines in early clinical trials.

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.

Expanded Physiologically-Based Pharmacokinetic Model of Rifampicin for Predicting Interactions With Drugs and an Endogenous Biomarker via Complex Mechanisms Including Organic Anion Transporting Polypeptide 1B Induction

As rifampicin can cause the induction and inhibition of multiple metabolizing enzymes and transporters, it has been challenging to accurately predict the complex drug–drug interactions (DDIs). We previously constructed a physiologically‐based pharmacokinetic (PBPK) model of rifampicin accounting for the components for the induction of cytochrome P450 (CYP) 3A/CYP2C9 and the inhibition of organic anion transporting polypeptide 1B (OATP1B). This study aimed to expand and verify the PBPK model for rifampicin by incorporating additional components for the induction of OATP1B and CYP2C8 and the inhibition of multidrug resistance protein 2. The established PBPK model was capable of accurately predicting complex rifampicin‐induced alterations in the profiles of glibenclamide, repaglinide, and coproporphyrin I (an endogenous biomarker of OATP1B activities) with various dosing regimens. Our comprehensive rifampicin PBPK model may enable quantitative prediction of DDIs across diverse potential victim drugs and endogenous biomarkers handled by multiple metabolizing enzymes and transporters.

[Quantitative assessment of the risk of OATP1B1/1B3-mediated drug-drug interactions]

Drug transporters play important roles in determining drug pharmacokinetics. Organic anion transporting polypeptides 1B1/1B3 (OATP1B1/1B3) are transporters mediating hepatic uptake of various anionic drugs. OATP1B1/1B3 activities are changed by genetic mutation and drug-drug interaction (DDI) that could lead to severe adverse reactions. Methods to address the precise DDI risk assessment have been developed in addition to the translational assessment from the results of in vitro studies. Using endogenous substrates as probes is an emerging approach that allows clinical assessment of the DDI risk in the early phase of drug development. Then, the clinical data will be subjected to the pharmacokinetic analysis using physiologically-based pharmacokinetic models to perform the more realistic DDI risk assessment with OATP1B1/1B3 substrate drugs. When drug targets are located inside the hepatocytes, DDI impact on the intrahepatic concentration is critical for their pharmacological actions. Positron emission tomography (PET) allows researchers to determine tissue concentration time profiles of the PET probe upon the inhibition of OATP1B1/1B3, and to estimate the change in kinetic parameter for each intrinsic process of hepatic elimination of PET probe. Integration of the clinical data into the PBPK model realizes more precise prediction of DDI impact on the pharmacokinetics of drugs, and their therapeutic effects.