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

Assessing Pleiotropic Effects of a Mixed-Mode Perpetrator Drug, Rifampicin, by Multiple Endogenous Biomarkers in Dogs

Rifampicin (RIF) is a mixed-mode perpetrator that produces pleiotropic effects on liver cytochrome P450 enzymes and drug transporters. To assess the complex drug-drug interaction liabilities of RIF in vivo, a known probe substrate, midazolam (MDZ), along with multiple endogenous biomarkers were simultaneously monitored in beagle dogs before and after a 7-day treatment period by RIF at 20 mg/kg per day. Confirmed by the reduced MDZ plasma exposure and elevated 4β-hydroxycholesterol (4β-HC, biomarker of CYP3A activities) level, CYP3A was significantly induced after repeated RIF doses, and such induction persisted for 3 days after cessation of the RIF administration. On the other hand, increased plasma levels of coproporphyrin (CP)-I and III [biomarkers of organic anion transporting polypeptides 1b (Oatp1b) activities] were observed after the first dose of RIF. Plasma CPs started to decline as RIF exposure decreased, and they returned to baseline 3 days after cessation of the RIF administration. The data suggested the acute (inhibitory) and chronic (inductive) effects of RIF on Oatp1b and CYP3A enzymes, respectively, and a 3-day washout period is deemed adequate to remove superimposed Oatp1b inhibition from CYP3A induction. In addition, apparent self-induction of RIF was observed as its terminal half-life was significantly altered after multiple doses. Overall, our investigation illustrated the need for appropriate timing of modulator dosing to differentiate between transporter inhibition and enzyme induction. As further indicated by the CP data, induction of Oatp1b activities was not likely after repeated RIF administration. SIGNIFICANCE STATEMENT: This investigation demonstrated the utility of endogenous biomarkers towards complex drug-drug interactions by rifampicin (RIF) and successfully determined the optimal timing to differentiate between transporter inhibition and enzyme induction. Based on experimental evidence, Oatp1b induction following repeated RIF administration was unlikely, and apparent self-induction of RIF elimination was observed.

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.

Utility of Minimal Physiologically Based Pharmacokinetic Models for Assessing Fractional Distribution, Oral Absorption, and Series-Compartment Models of Hepatic Clearance

Minimal physiologically based pharmacokinetic (mPBPK) models are physiologically relevant, require less information than full PBPK models, and offer flexibility in pharmacokinetics (PK). The well-stirred hepatic model (WSM) is commonly used in PBPK, whereas the more plausible dispersion model (DM) poses computational complexities. The series-compartment model (SCM) mimics the DM but is easier to operate. This work implements the SCM and mPBPK models for assessing fractional tissue distribution, oral absorption, and hepatic clearance using literature-reported blood and liver concentration-time data in rats for compounds mainly cleared by the liver. Further handled were various complexities, including nonlinear hepatic binding and metabolism, differing absorption kinetics, and sites of administration. The SCM containing one to five (n) liver subcompartments yields similar fittings and provides comparable estimates for hepatic extraction ratio (ER), prehepatic availability (Fg ), and first-order absorption rate constants (ka ). However, they produce decreased intrinsic clearances (CLint ) and liver-to-plasma partition coefficients (Kph ) with increasing n as expected. Model simulations demonstrated changes in intravenous and oral PK profiles with alterations in Kph and ka and with hepatic metabolic zonation. The permeability (PAMPA P) of the various compounds well explained the fitted fractional distribution (fd ) parameters. The SCM and mPBPK models offer advantages in distinguishing systemic, extrahepatic, and hepatic clearances. The SCM allows for incorporation of liver zonation and is useful in assessing changes in internal concentration gradients potentially masked by similar blood PK profiles. Improved assessment of intraorgan drug concentrations may offer insights into active moieties driving metabolism, biliary excretion, pharmacodynamics, and hepatic toxicity. SIGNIFICANCE STATEMENT: The minimal physiologically based pharmacokinetic model and the series-compartment model are useful in assessing oral absorption and hepatic clearance. They add flexibility in accounting for various drug- or system-specific complexities, including fractional distribution, nonlinear binding and saturable hepatic metabolism, and hepatic zonation. These models can offer improved insights into the intraorgan concentrations that reflect physiologically active moieties often driving disposition, pharmacodynamics, and toxicity.

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.

Physiologically-based pharmacokinetic pharmacodynamic parent-metabolite model of edoxaban to predict drug-drug-disease interactions: M4 contribution

This study aimed to develop a physiologically-based pharmacokinetic pharmacodynamic (PBPK/PD) parent-metabolite model of edoxaban, an oral anticoagulant with a narrow therapeutic index, and to predict pharmacokinetic (PK)/PD profiles and potential drug-drug-disease interactions (DDDIs) in patients with renal impairment. A whole-body PBPK model with a linear additive PD model of edoxaban and its active metabolite M4 was developed and validated in SimCYP for healthy adults with or without interacting drugs. The model was extrapolated to situations including renal impairment and drug-drug interactions (DDIs). Observed PK and PD data in adults were compared with predicted data. The effect of several model parameters on the PK/PD response of edoxaban and M4 was investigated in sensitivity analysis. The PBPK/PD model successfully predicted PK profiles of edoxaban and M4 as well as anticoagulation PD responses with or without the influence of interacting drugs. For patients with renal impairment, the PBPK model successfully predicted the fold change in each impairment group. Inhibitory DDI and renal impairment had a synergistic effect on the increased exposure of edoxaban and M4, and their downstream anticoagulation PD effect. Sensitivity analysis and DDDI simulation show that renal clearance, intestinal P-glycoprotein activity, and hepatic OATP1B1 activity are the major factors affecting edoxaban-M4 PK profiles and PD responses. Anticoagulation effect induced by M4 cannot be ignored when OATP1B1 is inhibited or downregulated. Our study provides a reasonable approach to adjust the dose of edoxaban in several complicated scenarios especially when M4 cannot be ignored due to decreased OATP1B1 activity.

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.

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.

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.

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.

Acute and Chronic Effects of Rifampin on Letermovir Suggest Transporter Inhibition and Induction Contribute to Letermovir Pharmacokinetics

Rifampin has acute inhibitory and chronic inductive effects that can cause complex drug-drug interactions. Rifampin inhibits transporters including organic-anion-transporting polypeptide (OATP)1B and P-glycoprotein (P-gp), and induces enzymes and transporters including cytochrome P450 3A, UDP-glucuronosyltransferase (UGT)1A, and P-gp. This study aimed at separating inhibitory and inductive effects of rifampin on letermovir disposition and elimination (indicated for cytomegalovirus prophylaxis in hematopoietic stem cell transplant recipients). Letermovir is a substrate of UGT1A1/3, P-gp, and OATP1B, with its clearance primarily mediated by OATP1B. Letermovir (single-dose) administered with rifampin (single-dose) resulted in increased letermovir exposure through transporter inhibition. Chronic coadministration with rifampin (inhibition plus potential OATP1B induction) resulted in modestly decreased letermovir exposure versus letermovir alone. Letermovir administered 24 hours after last rifampin dose (potential OATP1B induction) resulted in markedly decreased letermovir exposure. These data suggest rifampin may induce transporters that clear letermovir; the modestly reduced letermovir exposure with chronic rifampin coadministration likely reflects the net effect of inhibition and induction. OATP1B endogenous biomarkers coproporphyrin (CP) I and glycochenodeoxycholic acid-sulfate (GCDCA-S) were also analyzed; their exposures increased after single-dose rifampin plus letermovir, consistent with OATP1B inhibition and prior reports of inhibition by rifampin alone. CP I and GCDCA-S exposures were substantially reduced with letermovir administered 24 hours after the last dose of rifampin versus letermovir plus chronic rifampin coadministration, This study suggests that OATP1B induction may contribute to reduced letermovir exposure after chronic rifampin administration, although given the complexity of letermovir disposition, alternative mechanisms are not fully excluded.

Physiologically Based Pharmacokinetic Modeling of Transporter-Mediated Hepatic Disposition of Imaging Biomarker Gadoxetate in Rats

Physiologically based pharmacokinetic (PBPK) models are increasingly used in drug development to simulate changes in both systemic and tissue exposures that arise as a result of changes in enzyme and/or transporter activity. Verification of these model-based simulations of tissue exposure is challenging in the case of transporter-mediated drug–drug interactions (tDDI), in particular as these may lead to differential effects on substrate exposure in plasma and tissues/organs of interest. Gadoxetate, a promising magnetic resonance imaging (MRI) contrast agent, is a substrate of organic-anion-transporting polypeptide 1B1 (OATP1B1) and multidrug resistance-associated protein 2 (MRP2). In this study, we developed a gadoxetate PBPK model and explored the use of liver-imaging data to achieve and refine in vitro–in vivo extrapolation (IVIVE) of gadoxetate hepatic transporter kinetic data. In addition, PBPK modeling was used to investigate gadoxetate hepatic tDDI with rifampicin i.v. 10 mg/kg. In vivo dynamic contrast-enhanced (DCE) MRI data of gadoxetate in rat blood, spleen, and liver were used in this analysis. Gadoxetate in vitro uptake kinetic data were generated in plated rat hepatocytes. Mean (%CV) in vitro hepatocyte uptake unbound Michaelis–Menten constant (K_m,u) of gadoxetate was 106 μM (17%) (n = 4 rats), and active saturable uptake accounted for 94% of total uptake into hepatocytes. PBPK–IVIVE of these data (bottom-up approach) captured reasonably systemic exposure, but underestimated the in vivo gadoxetate DCE–MRI profiles and elimination from the liver. Therefore, in vivo rat DCE–MRI liver data were subsequently used to refine gadoxetate transporter kinetic parameters in the PBPK model (top-down approach). Active uptake into the hepatocytes refined by the liver-imaging data was one order of magnitude higher than the one predicted by the IVIVE approach. Finally, the PBPK model was fitted to the gadoxetate DCE–MRI data (blood, spleen, and liver) obtained with and without coadministered rifampicin. Rifampicin was estimated to inhibit active uptake transport of gadoxetate into the liver by 96%. The current analysis highlighted the importance of gadoxetate liver data for PBPK model refinement, which was not feasible when using the blood data alone, as is common in PBPK modeling applications. The results of our study demonstrate the utility of organ-imaging data in evaluating and refining PBPK transporter IVIVE to support the subsequent model use for quantitative evaluation of hepatic tDDI.

Liver Fibrosis Indices for the Prediction of Mortality in Korean Subjects: A 16-Year Prospective Cohort Study

Context

Nonalcoholic fatty liver disease (NAFLD) and its progression to liver fibrosis are related to higher mortality.

Objective

We investigated whether noninvasive indices of NAFLD and liver fibrosis could predict mortality in a Korean prospective cohort study.

Methods

We followed 4163 subjects from the Korean Genome and Epidemiology Study biannually over 16 years. Cox proportional hazards regression was used to calculate the hazard ratios (HRs) of NAFLD or liver fibrosis indices in the total group of subjects and subgroups according to body mass index (BMI) and glucose metabolism status.

Results

The mean age (± SD) of the subjects was 55.7 ± 8.7 years and 39.2% were men. During a median follow-up period of 15.6 years, 643 subjects (15.4%) died. The Fibrosis-4 (FIB-4), NAFLD fibrosis score (NFS), and aspartate aminotransferase to platelet ratio index were consistently higher in deceased subjects regardless of baseline glucose metabolism status. The FIB-4 and NFS displayed acceptable discrimination power for mortality, with area under the receiver operating characteristic curve values of 0.686 and 0.666, respectively. The adjusted HRs for FIB-4 and NFS were 1.41 (95% CI, 1.18-1.68) and 1.43 (95% CI, 1.21-1.68), respectively. Both FIB-4 and NFS were significantly associated with liver-specific mortality but not cardiovascular mortality. The association between mortality with fibrosis indices were more prominent in subjects with a lower BMI (<25 kg/m²).

Conclusion

Noninvasive indices of liver fibrosis might be a significant predictor of all-cause and liver-specific mortality in Korean subjects.

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.

Evaluation of Pharmacokinetic Drug-Drug Interactions: A Review of the Mechanisms, In Vitro and In Silico Approaches

Pharmacokinetic drug–drug interactions (DDIs) occur when a drug alters the absorption, transport, distribution, metabolism or excretion of a co-administered agent. The occurrence of pharmacokinetic DDIs may result in the increase or the decrease of drug concentrations, which can significantly affect the drug efficacy and safety in patients. Enzyme-mediated DDIs are of primary concern, while the transporter-mediated DDIs are less understood but also important. In this review, we presented an overview of the different mechanisms leading to DDIs, the in vitro experimental tools for capturing the factors affecting DDIs, and in silico methods for quantitative predictions of DDIs. We also emphasized the power and strategy of physiologically based pharmacokinetic (PBPK) models for the assessment of DDIs, which can integrate relevant in vitro data to simulate potential drug interaction in vivo. Lastly, we pointed out the future directions and challenges for the evaluation of pharmacokinetic DDIs.

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.

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.

Intestinal P-gp and Putative Hepatic OATP1B Induction: International Transporter Consortium Perspective on Drug Development Implications

There is an increasing interest in transporter induction (i.e., decreased systemic drug exposure due to increased efflux-limited absorption or transporter-mediated clearance) as a mechanism of drug-drug interactions (DDIs), although evidence of clinical relevance is still evolving. Intestinal P-glycoprotein (P-gp) and hepatic organic anion transporting polypeptides 1B (OATP1B) can be important determinants of drug absorption and disposition, as well as targets for DDIs. Current data indicate that intestinal P-gp protein levels can be induced up to threefold to fourfold in humans primarily with pregnane X receptor (PXR) activators, and that this induction can decrease the systemic exposure of drugs with P-gp efflux-limited absorption (e.g., ≤ 67% decrease in the exposure of total dabigatran following rifampin multiple oral dosing). Evaluation of the clinical relevance of P-gp induction as a DDI mechanism must consider the induction potential of the perpetrator drug for P-gp and attenuation of exposure of the victim drug in the context of its therapeutic window. Practical drug development recommendations are provided herein. Reports are contradictory on OATP1B induction by PXR activators in human hepatocytes and liver biopsies. Some clinical investigations demonstrated that rifampin pretreatment decreased exposure of OATP1B substrates, while other studies found no differences, and the potential involvement of other mechanisms in these observed DDIs cannot be definitively ruled out. Thus, further studies are needed to understand hepatic OATP1B induction and potential involvement of other mechanisms contributing to reduced exposure of OATP1B substrates. This review critically summarizes the state-of-the-art on intestinal P-gp and hepatic OATP1B induction, and highlights implications for drug development.

Hepatic Transporter Alterations by Nuclear Receptor Agonist T0901317 in Sandwich-Cultured Human Hepatocytes: Proteomic Analysis and PBPK Modeling to Evaluate Drug-Drug Interaction Risk

In vitro approaches for predicting drug-drug interactions (DDIs) caused by alterations in transporter protein regulation are not well established. However, reports of transporter regulation via nuclear receptor (NR) modulation by drugs are increasing. This study examined alterations in transporter protein levels in sandwich-cultured human hepatocytes (SCHH; n = 3 donors) measured by liquid chromatography-tandem mass spectrometry-based proteomic analysis after treatment with N-[4-(1,1,1,3,3,3-hexafluoro-2-hydroxypropan-2-yl)phenyl]-N-(2,2,2-trifluoroethyl)benzenesulfonamide (T0901317), the first described synthetic liver X receptor agonist. T0901317 treatment (10 μM, 48 hours) decreased the levels of organic cation transporter (OCT) 1 (0.22-, 0.43-, and 0.71-fold of control) and organic anion transporter (OAT) 2 (0.38-, 0.38-, and 0.53-fold of control) and increased multidrug resistance protein (MDR) 1 (1.37-, 1.48-, and 1.59-fold of control). The induction of NR downstream gene expression supports the hypothesis that T0901317 off-target effects on farnesoid X receptor and pregnane X receptor activation are responsible for the unexpected changes in OCT1, OAT2, and MDR1. Uptake of the OCT1 substrate metformin in SCHH was decreased by T0901317 treatment. Effects of decreased OCT1 levels on metformin were simulated using a physiologically-based pharmacokinetic (PBPK) model. Simulations showed a clear decrease in metformin hepatic exposure resulting in a decreased pharmacodynamic effect. This DDI would not be predicted by the modest changes in simulated metformin plasma concentrations. Altogether, the current study demonstrated that an approach combining SCHH, proteomic analysis, and PBPK modeling is useful for revealing tissue concentration-based DDIs caused by unexpected regulation of hepatic transporters by NR modulators. SIGNIFICANCE STATEMENT: This study utilized an approach combining sandwich-cultured human hepatocytes, proteomic analysis, and physiologically based pharmacokinetic modeling to evaluate alterations in pharmacokinetics (PK) and pharmacodynamics (PD) caused by transporter regulation by nuclear receptor modulators. The importance of this approach from a mechanistic and clinically relevant perspective is that it can reveal drug-drug interactions (DDIs) caused by unexpected regulation of hepatic transporters and enable prediction of altered PK and PD changes, especially for tissue concentration-based DDIs.

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.

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.