Physiologically based pharmacokinetic modeling to predict transporter-mediated clearance and distribution of pravastatin in humans

Physiologically Based Pharmacokinetic Model of OATP1B Substrates with a Nonlinear Mixed Effect Approach: Estimating Empirical In Vitro-to-In Vivo Scaling Factors

BACKGROUND AND OBJECTIVE

Physiologically based pharmacokinetic (PBPK) models are valuable for translating in vitro absorption, distribution, metabolism, and excretion (ADME) data to predict clinical pharmacokinetics, and can enable discovery and early clinical stages of pharmaceutical research. However, in predicting pharmacokinetics of organic anion transporting polypeptide (OATP) 1B substrates based on in vitro transport and metabolism data, PBPK models typically require additional empirical in vitro-to-in vivo scaling factors (ESFs) in order to accurately recapitulate observed clinical profiles. As model simulation is very sensitive to ESFs, a critical evaluation of ESF estimation is prudent. Previously studies have applied classic 'two-stage' and 'naïve pooled data' approaches in identifying a set of compound independent ESFs. However, the 'two-stage' approach has the parameter identification issue in separately fitting data for individual compounds, while the 'naïve pooled data' approach ignores interstudy variability, leading to potentially biased ESF estimates.

METHODS

In this study, we have applied a nonlinear mixed-effect approach in estimating ESF of the PBPK model and incorporated additional data from 86 runs of in vitro uptake assay and 49 clinical studies of 12 training compounds in model development to further enhance the translation of in vitro data to predict the pharmacokinetics of OATP1B substrate drugs. To test predication accuracy of the model, a 'leave-one-out' analysis has been performed.

RESULTS

The established model can reasonably describe the clinical observations, with both mean values and interstudy variabilities quantified for ESF and volume of distribution parameters. The mean estimates are largely consistent with values in the previous reports. The interstudy variabilities of these parameters are estimated to be at least 50% (as coefficient of variation). Most compounds can be reasonably predicted in the 'leave-one-out' analysis.

CONCLUSION

This study improves the confidence in predicting the pharmacokinetics of OATP1B substrates in individual studies of small sample sizes, and quantifies the variability associated with the prediction.

After 50 Years of Hepatic Clearance Models, Where Should We Go from Here? Improvements and Implications for Physiologically Based Pharmacokinetic Modeling

There is overwhelming preference for application of the unphysiologic, well-stirred model (WSM) over the parallel tube model (PTM) and dispersion model (DM) to predict hepatic drug clearance, CLH , despite that liver blood flow is dispersive and closer to the DM in nature. The reasoning is the ease in computation relating the hepatic intrinsic clearance ( CLint ), hepatic blood flow ( QH ), unbound fraction in blood ( fub ) and the transmembrane clearances ( CLin and CLef ) to CLH for the WSM. However, the WSM, being the least efficient liver model, predicts a lower EH that is associated with the in vitro CLint ( Vmax / Km ), therefore requiring scale-up to predict CLH in vivo. By contrast, the miniPTM, a three-subcompartment tank-in-series model of uniform enzymes, closely mimics the DM and yielded similar patterns for CLint versus EH , substrate concentration [S] , and KL / B , the tissue to outflow blood concentration ratio. We placed these liver models nested within physiologically based pharmacokinetic models to describe the kinetics of the flow-limited, phenolic substrate, harmol, using the WSM (single compartment) and the miniPTM and zonal liver models (ZLMs) of evenly and unevenly distributed glucuronidation and sulfation activities, respectively, to predict CLH For the same, given CLint ( Vmax and Km ), the WSM again furnished the lowest extraction ratio ( EH,WSM = 0.5) compared with the miniPTM and ZLM (>0.68). Values of EH,WSM were elevated to those for EH, PTM and EH, ZLM when the Vmax s for sulfation and glucuronidation were raised 5.7- to 1.15-fold. The miniPTM is easily manageable mathematically and should be the new normal for liver/physiologic modeling. SIGNIFICANCE STATEMENT: Selection of the proper liver clearance model impacts strongly on CLH predictions. The authors recommend use of the tank-in-series miniPTM (3 compartments mini-parallel tube model), which displays similar properties as the dispersion model (DM) in relating CLint and [ S ] to CLH as a stand-in for the DM, which best describes the liver microcirculation. The miniPTM is readily modified to accommodate enzyme and transporter zonation.

The Effect of Inflammatory Bowel Disease and Irritable Bowel Syndrome on Pravastatin Oral Bioavailability: In vivo and in silico evaluation using bottom-up wbPBPK modeling

The common disorders irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD) can modify the drugs' pharmacokinetics via their induced pathophysiological changes. This work aimed to investigate the impact of these two diseases on pravastatin oral bioavailability. Rat models for IBS and IBD were used to experimentally test the effects of IBS and IBD on pravastatin pharmacokinetics. Then, the observations made in rats were extrapolated to humans using a mechanistic whole-body physiologically-based pharmacokinetic (wbPBPK) model. The rat in vivo studies done herein showed that IBS and IBD decreased serum albumin (> 11% for both), decreased PRV binding in plasma, and increased pravastatin absolute oral bioavailability (0.17 and 0.53 compared to 0.01) which increased plasma, muscle, and liver exposure. However, the wbPBPK model predicted muscle concentration was much lower than the pravastatin toxicity thresholds for myotoxicity and rhabdomyolysis. Overall, IBS and IBD can significantly increase pravastatin oral bioavailability which can be due to a combination of increased pravastatin intestinal permeability and decreased pravastatin gastric degradation resulting in higher exposure. This is the first study in the literature investigating the effects of IBS and IBD on pravastatin pharmacokinetics. The high interpatient variability in pravastatin concentrations as induced by IBD and IBS can be reduced by oral administration of pravastatin using enteric-coated tablets. Such disease (IBS and IBD)-drug interaction can have more drastic consequences for narrow therapeutic index drugs prone to gastric degradation, especially for drugs with low intestinal permeability.

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine

INTRODUCTION

Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties.

AREAS COVERED

The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.

EXPERT OPINION

AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

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.

Evaluation of the non-linearity of NA808 in liver not reflected in plasma using a rat pharmacokinetic study and PBPK modelling

When NA808, a potent HCV replication inhibitor, was intravenously administered to rats, it was distributed to the liver. The AUC ratio in the liver of 20 mg/kg to 2 mg/kg was greater than the dose ratio, whereas exposure in plasma was increased in a dose-proportional manner. Saturation of biliary excretion was also shown at 20 mg/kg.NA808 was revealed to be a substrate for both OATP1B and MRP2 transporters by an in vitro study using OATP1B1-MRP2 expressing cells. [14C]NA808 was taken up into the cells by OATP1B1 and excreted from cells by MRP2 efficiently (Papp ratio: 24.2-70.2). The Papp ratio decreased with increasing NA808 concentration.PBPK modelling was constructed to display the blood and liver concentration time profile and biliary excretion of NA808. This model analysis was able to reproduce the pharmacokinetics in rats; the degree of increase in the liver exposure from 2 to 20 mg/kg was more than dose-proportional and was greater than the increase in the blood exposure due to saturation of efflux transporters.In drug development, to avoid unexpected toxicity in tissues, it is important to consider the potential for tissue non-linearity with linear plasma exposure based on pre-clinical data and PBPK modelling.

Evaluation of the drug disposition of RO7049389 with in vitro data and human mass balance supported by physiologically based pharmacokinetic modelling

AIMS

RO7049389 (linvencorvir) is a developmental oral treatment for chronic hepatitis B virus infection. The aim of this work was to conduct mass balance (MB) and absolute bioavailability (BA) analyses in healthy volunteers, alongside in vitro evaluations of the metabolism of RO7049389 and a major circulating active metabolite M5 in human hepatocytes, and physiologically based pharmacokinetic (PBPK) modelling to refine the underlying drug disposition paradigm.

METHODS

Participants in the clinical study (MB: Caucasian, male, n = 6; BA: Caucasian and Asian, male and female, n = 16, 8 in each ethnic groups) received oral [14 C] or unlabelled RO7049389 (600/1000 mg) followed by 100 μg intravenous [13 C]RO7049389. Metabolic pathways with fractions metabolized-obtained from the in vitro incubation results of 10 μM [14 C]RO7049389 and 1 μM M5 with (long-term cocultured) human hepatocytes in the absence and presence of the cytochrome P450 3A4 (CYP3A4) inhibitor itraconazole-were used to complement the PBPK models, alongside the clinical MB and BA data.

RESULTS

The model performance in predicting the pharmacokinetic profiles of RO7049389 and M5 aligned with clinical observations in Caucasians and was also successfully applied to Asians. Accordingly, the drug disposition pathways for RO7049389 were postulated with newly characterized estimates of the fractions: biliary excretion by P-glycoprotein (~41%), direct glucuronidation via uridine 5'-diphosphoglucuronosyltransferase 1A3 (~11%), hexose conjugation (~6%), oxidation by CYP3A4 (~28%) and other oxidation reactions (~9%).

CONCLUSION

These results support the ongoing clinical development program for RO7049389 and highlight the broader value of PBPK and MB analyses in drug development.

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.

Albumin-bound kynurenic acid is an appropriate endogenous biomarker for assessment of the renal tubular OATs-MRP4 channel

Renal tubular secretion mediated by organic anion transporters (OATs) and the multidrug resistance-associated protein 4 (MRP4) is an important means of drug and toxin excretion. Unfortunately, there are no biomarkers to evaluate their function. The aim of this study was to identify and characterize an endogenous biomarker of the renal tubular OATs-MRP4 channel. Twenty-six uremic toxins were selected as candidate compounds, of which kynurenic acid was identified as a potential biomarker by assessing the protein-binding ratio and the uptake in OAT1-, OAT3-, and MRP4-overexpressing cell lines. OAT1/3 and MRP4 mediated the transcellular vectorial transport of kynurenic acid in vitro. Serum kynurenic acid concentration was dramatically increased in rats treated with a rat OAT1/3 (rOAT1/3) inhibitor and in rOAT1/3 double knockout (rOAT1/3-/-) rats, and the renal concentrations were markedly elevated by the rat MRP4 (rMRP4) inhibitor. Kynurenic acid was not filtered at the glomerulus (99% of albumin binding), and was specifically secreted in renal tubules through the OAT1/3-MRP4 channel with an appropriate affinity (Km) (496.7 μM and 382.2 μM for OAT1 and OAT3, respectively) and renal clearance half-life (t1/2) in vivo (3.7 ± 0.7 h). There is a strong correlation in area under the plasma drug concentration-time curve (AUC0-t) between cefmetazole and kynurenic acid, but not with creatinine, after inhibition of rOATs. In addition, the phase of increased kynurenic acid level is earlier than that of creatinine in acute kidney injury process. These results suggest that albumin-bound kynurenic acid is an appropriate endogenous biomarker for adjusting the dosage of drugs secreted by this channel or predicting kidney injury.

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.

Exploring the Pharmacokinetic Mysteries of the Liver: Application of Series Compartment Models of Hepatic Elimination

Among the basic hepatic clearance models, the dispersion model (DM) is the most physiologically sound compared with the well-stirred model and the parallel tube model. However, its application in physiologically-based pharmacokinetic (PBPK) modeling has been limited due to computational complexities. The series compartment models (SCM) of hepatic elimination that treats the liver as a cascade of well-stirred compartments connected by hepatic blood flow exhibits some mathematical similarities to the DM but is easier to operate. This work assesses the quantitative correlation between the SCM and DM and demonstrates the operation of the SCM in PBPK with the published single-dose blood and liver concentration-time data of six flow-limited compounds. The predicted liver concentrations and the estimated intrinsic clearance (CLint ) and PBPK-operative tissue-to-plasma partition coefficient (Kp ) values were shown to depend on the number of liver sub-compartments (n) and hepatic enzyme zonation in the SCM. The CLint and Kp decreased with increasing n, with more remarkable differences for drugs with higher hepatic extraction ratios. Given the same total CLint , the SCM yields a higher Kp when the liver perivenous region exhibits a lower CLint as compared with a high CLint at this region. Overall, the SCM nicely approximates the DM in characterizing hepatic elimination and offers an alternative flexible approach as well as providing some insights regarding sequential drug concentrations in the liver. SIGNIFICANCE STATEMENT: The SCM nicely approximates the DM when applied in PBPK for characterizing hepatic elimination. The number of liver sub-compartments and hepatic enzyme zonation are influencing factors for the SCM resulting in model-dependent predictions of total/internal liver concentrations and estimates of CLint and the PBPK-operative Kp . Such model-dependency may have an impact when the SCM is used for in vitro-to-in vivo extrapolation (IVIVE) and may also be relevant for PK/PD/toxicological effects when it is the driving force for such responses.

Novel approach for verification of a human PBPK modeling strategy using chimeric mice in the health risk assessment of epyrifenacil

In the human risk assessment by physiologically based pharmacokinetic modeling (PBPK), verification of the modeling strategy and confirmation of the reliability of the output data are important when the clinical data are not available. A new herbicide, epyrifenacil, is metabolized to S-3100-CA in mammals and causes hepatotoxicity in mice. S-3100-CA is transferred to the liver by transporters and eliminated by biliary excretion and metabolism. In the previous human PBPK research, we succeeded in predicting S-3100-CA pharmacokinetics by obtaining human hepatic parameters from chimeric mice with humanized liver after we checked the model's quantitative performance using mouse experimental data. To further enhance the reliability of human PBPK data, verification of the following two points was considered effective: 1) verification of model applicability to pharmacokinetics prediction in multiple animal species, and 2) verification of the parameter acquisition methods. In this study, we applied the same modeling strategy to rats, i.e., we obtained rat hepatic parameters for PBPK from chimeric mice with rat hepatocytes, not from rats. As the simulation results, rat internal dosimetry was precisely reproduced, although it tended to be slightly overestimated by approximately two times. From the results of the sensitivity analysis, this overestimation was mainly due to hepatic parameters from chimeric mice. Therefore, it is suggested that a similar slight prediction error may occur also in human PBPK using chimeric mice, but considering the degree of error, it can be said that our modeling strategy is robust and the predicted human internal dosimetry in the previous research is reliable.

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.

Evodiamine decreased the systemic exposure of pravastatin in non-alcoholic steatohepatitis rats due to the up-regulation of hepatic OATPs

CONTEXT

Patients with non-alcoholic steatohepatitis (NASH) may have a simultaneous intake of pravastatin and evodiamine-containing herbs.

OBJECTIVE

The effect of evodiamine on the pharmacokinetics of pravastatin and its potential mechanisms were investigated in NASH rats.

MATERIALS AND METHODS

The NASH model was conducted with feeding a methionine choline-deficient (MCD) diet for 8 weeks. Sprague-Dawley rats were randomised equally (n = 6) into NASH group, evodiamine group (10 mg/kg), pravastatin group (10 mg/kg), and evodiamine (10 mg/kg) + pravastatin (10 mg/kg) group. Normal control rats were fed a standard diet. Effects of evodiamine on the pharmacokinetics, distribution, and uptake of pravastatin were investigated.

RESULTS

Evodiamine decreased C_max (159.43 ± 26.63 vs. 125.61 ± 22.17 μg/L), AUC_0-t (18.17 ± 2.52 vs. 14.91 ± 2.03 mg/min/L) and AUC_0-∞ (22.99 ± 2.62 vs. 19.50 ± 2.31 mg/min/L) of orally administered pravastatin in NASH rats, but had no significant effect in normal rats. Evodiamine enhanced the uptake (from 154.85 ± 23.17 to 198.48 ± 26.31 pmol/mg protein) and distribution (from 736.61 ± 108.07 to 911.89 ± 124.64 ng/g tissue) of pravastatin in NASH rat liver. The expression of Oatp1a1, Oatp1a4, and Oatp1b2 was up-regulated 1.48-, 1.38-, and 1.51-fold by evodiamine. Evodiamine decreased the levels of IL-1β, IL-6, and TNF-α by 27.82%, 24.76%, and 29.72% in NASH rats, respectively.

DISCUSSION AND CONCLUSIONS

Evodiamine decreased the systemic exposure of pravastatin by up-regulating the expression of OATPs. These results provide a reference for further validation of this interaction in humans.

Analysis of Time-Dependent Pharmacokinetics Using In Vitro-In Vivo Extrapolation and Physiologically Based Pharmacokinetic Modeling

SCR430, a sorafenib derivative, is an investigational drug exhibiting anti-tumor action. This study aimed to have a mechanistic understanding of SCR430's time-dependent pharmacokinetics (TDPK) through an ex vivo study combined with an in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) modeling. A non-compartmental pharmacokinetic analysis was performed after intravenous SCR430 administration in female Sprague-Dawley rats for a control group (no treatment), a vehicle group (vehicle only, 14 days, PO), and a repeated-dosing group (SCR430, 30 mg/kg/day, 14 days, PO). In addition, hepatic uptake and metabolism modulation were investigated using isolated hepatocytes from each group of rats. The minimal PBPK model based on IVIVE was constructed to explain SCR430's TDPK. Repeated SCR430 administration decreased the systemic exposure by 4.4-fold, which was explained by increased hepatic clearance (4.7-fold). The ex vivo study using isolated hepatocytes from each group suggested that the increased hepatic uptake (9.4-fold), not the metabolic activity, contributes to the increased hepatic clearance. The minimal PBPK modeling based on an ex vivo study could explain the decreased plasma levels after the repeated doses. The current study demonstrates the TDPK after repeated dosing by hepatic uptake induction, not hepatic metabolism, as well as the effectiveness of an ex vivo approach combined with IVIVE and PBPK modeling to investigate the TDPK.

Recent advances in the translation of drug metabolism and pharmacokinetics science for drug discovery and development

Drug metabolism and pharmacokinetics (DMPK) is an important branch of pharmaceutical sciences. The nature of ADME (absorption, distribution, metabolism, excretion) and PK (pharmacokinetics) inquiries during drug discovery and development has evolved in recent years from being largely descriptive to seeking a more quantitative and mechanistic understanding of the fate of drug candidates in biological systems. Tremendous progress has been made in the past decade, not only in the characterization of physiochemical properties of drugs that influence their ADME, target organ exposure, and toxicity, but also in the identification of design principles that can minimize drug-drug interaction (DDI) potentials and reduce the attritions. The importance of membrane transporters in drug disposition, efficacy, and safety, as well as the interplay with metabolic processes, has been increasingly recognized. Dramatic increases in investments on new modalities beyond traditional small and large molecule drugs, such as peptides, oligonucleotides, and antibody-drug conjugates, necessitated further innovations in bioanalytical and experimental tools for the characterization of their ADME properties. In this review, we highlight some of the most notable advances in the last decade, and provide future perspectives on potential major breakthroughs and innovations in the translation of DMPK science in various stages of drug discovery and development.

Understanding Inter-individual Variability in the Drug Interaction of a Highly Extracted CYP1A2 Substrate Tizanidine: Application of a Permeability-limited Multi-compartment Liver Model in a Population Based PBPK Framework

Tizanidine, a centrally acting skeletal muscle relaxant, is predominantly metabolised by CYP1A2 and undergoes extensive hepatic first-pass metabolism following oral administration. As a highly extracted drug, the systemic exposure to tizanidine exhibits considerable inter-individual variability and is altered substantially when co-administered with CYP1A2 inhibitors or inducers. The aim of the current study was to compare the performance of a permeability-limited multi-compartment liver (PerMCL) model, which operates as an approximation of the dispersion model (DM), and the well-stirred model (WSM) for predicting tizanidine DDIs. Physiologically-based pharmacokinetic (PBPK) models were developed for tizanidine, incorporating the PerMCL model and the WSM, respectively, to simulate the interaction of tizanidine with a range of CYP1A2 inhibitors and inducers. While the WSM showed a tendency to under-predict the fold change of tizanidine AUC (AUC ratio) in the presence of perpetrators, the use of PerMCL model increased precision (absolute average-fold error: 1.32 - 1.42 versus 1.58) and decreased bias (average-fold error: 0.97 - 1.25 versus 0.63) for the predictions of mean AUC ratios as compared to the WSM. The PerMCL model captured the observed range of individual AUC ratios of tizanidine as well as the correlation between individual AUC ratios and CYP1A2 activities without interactions, whereas the WSM was not able to capture these. The results demonstrate the advantage of using the PerMCL model over the WSM in predicting the magnitude and inter-individual variability of DDIs for a highly extracted sensitive substrate tizanidine. Significance Statement This study demonstrates the advantages of the permeability-limited multi-compartment liver (PerMCL) model, which operates as an approximation of the dispersion model (DM), in mitigating the tendency of the well-stirred model (WSM) to under-predict the magnitude and variability of DDIs of a highly extracted CYP1A2 substrate tizanidine when it is administered with CYP1A2 inhibitors or inducers. The PBPK modelling approach described herein is valuable to the understanding of drug interactions of highly extracted substrates and the source of its inter-individual variability.

Integration of Engineered Delivery with the Pharmacokinetics of Medical Candidates via Physiology-Based Pharmacokinetics

Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic computational model that can be used to predict a drug product's ADME (absorption, distribution, metabolism, and excretion) and pharmacokinetics (PK). In recent years, PBPK modeling and simulation has been used increasingly to address many biopharmaceutics and clinical pharmacology questions, such as the effect of formulations, intrinsic factors (age, organ dysfunction, etc.), and extrinsic factors (comedications, food) on the PK of an investigational drug product. In this chapter, we will briefly introduce various PBPK models for ADME prediction and general procedures for PBPK modeling and simulations. The readers are encouraged to read updated literature on new applications of PBPK modeling and simulation which is still an emerging area in pharmaceutical 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.

Acyclovir Brain Disposition: Interactions with P-gp, Bcrp, Mrp2, and Oat3 at the Blood-Brain Barrier

BACKGROUND AND OBJECTIVE

Acyclovir is effective in treating herpes simplex virus infections of the central nervous system. The purpose of this study was to investigate the interactions between acyclovir and the efflux pumps P-glycoprotein (P-gp), breast cancer resistance protein (Bcrp), multidrug resistance protein 2 (Mrp2), and organic anion transporter 3 (Oat3) at the blood-brain barrier (BBB).

METHODS

Acyclovir concentrations in the blood and brain were evaluated by microdialysis and high-performance liquid chromatography. Acyclovir pharmacokinetic parameters, including the area under the unbound blood concentration-time curve (AUCu,blood), the area under the unbound brain concentration-time curve (AUCu,brain), and the ratio of AUCu,brain to AUCu,blood (Kp.uu.brain), were evaluated in the presence and absence of elacridar (P-gp/Bcrp inhibitor, 7.5 mg/kg), tariquidar (P-gp/Bcrp inhibitor, 7.5 mg/kg), MK571 (Mrp2 inhibitor, 7.5 mg/kg), cyclosporine (P-gp/Bcrp/Mrp2 inhibitor, 25 mg/kg), and probenecid (Oat3 inhibitor, 50 mg/kg).

RESULTS

The average AUCu,blood, AUCu,brain, and Kp.uu.brain in rats who received acyclovir (25 mg/kg, intravenous) alone were 1377.7 min · μg/ml, 435.4 min · μg/ml, and 31.6%, respectively. Probenecid drastically increased the AUCu,blood of acyclovir 1.73-fold, whereas coadministration with elacridar, tariquidar, MK571, and cyclosporine did not alter the blood pharmacokinetic parameters of acyclovir. Elacridar, tariquidar, MK571, cyclosporine, and probenecid significantly increased the AUCu,brain of acyclovir 1.51-, 1.54-, 1.47-, 1.95-, and 2.34-fold, respectively. Additionally, the Kp.uu.brain of acyclovir markedly increased 1.48-, 1.63-, 1.39-, 1.90-, and 1.35-fold following elacridar, tariquidar, MK571, cyclosporine, and probenecid administration, respectively.

CONCLUSION

The present study demonstrated that P-gp, Bcrp, Mrp2, and Oat3 inhibition increased the penetration of acyclovir across the BBB, supporting the hypothesis that these efflux pumps restrict the distribution of acyclovir in the brain.

Prediction of the human pharmacokinetics of epyrifenacil and its major metabolite, S-3100-CA, by a physiologically based pharmacokinetic modeling using chimeric mice with humanized liver

Human internal dosimetry of pesticides is essential in the risk assessment when toxicity has been confirmed in laboratory animals. While human toxicokinetics data of pesticides are hardly obtained intendedly, the use of physiologically based pharmacokinetic (PBPK) modeling has become important for predicting human internal dosimetry. Especially, when the compound exhibits complicated pharmacokinetics via active uptake, metabolism, and biliary excretion in liver, it is difficult to obtain these hepatic parameters only by the in vitro experiments. Epyrifenacil, a new herbicide, is rapidly metabolized to S-3100-CA (CA) in mammals and causes hepatotoxicity in mice. CA is eliminated from the systemic circulation by biliary excretion and metabolism in liver. Although uptake of CA by transporters is observed in mouse primary hepatocytes, significantly less of it is observed in human primary hepatocytes. In order to evaluate human internal dosimetry of CA, a precise PBPK model was developed. To obtain human hepatic parameters, i.e., hepatic elimination intrinsic clearance via biliary excretion and metabolism, we used chimeric mice with humanized liver as a model to reproduce the complicated pharmacokinetics of CA in humans. After we developed a mouse PBPK model, by replacing mouse parameters with those of humans, we calculated CA concentration in human liver. Comparing the predicted CA exposure in human liver with the measured values in mice, we demonstrated a clear interspecies difference of approximately 4 times lower C_max and AUC in humans. This result suggested that the risk of hepatotoxicity is less in humans than in mice.

Advancing Estimation of Hepatobiliary Clearances in Physiologically Based Pharmacokinetic Models of Rosuvastatin Using Human Hepatic Concentrations

PURPOSE

To estimate hepatobiliary clearances of rosuvastatin via simultaneously fitting to reported human positron emission tomography (PET) data in the liver and gallbladder.

METHODS

A hepatobiliary model incorporating five intrinsic hepatobiliary clearances (active uptake clearance at the sinusoidal membrane, efflux clearance by passive diffusion through the sinusoidal membrane, influx clearance by passive diffusion through sinusoidal membrane, clearance of biliary excretion at the canalicular membrane, and intercompartment clearance from the intrahepatic bile duct to the gallbladder) and three compartments (liver, intrahepatic bile duct, and gallbladder) was developed to simultaneously fit rosuvastatin liver and gallbladder data from a representative subject reported by Billington et al. (1). Two liver blood supply input functions, arterial input function and dual input function (using peripheral venous as an alternative to portal vein), were assessed. Additionally, the predictive performance between the established model and four reported models trained with only systemic exposure data, was evaluated by comparing simulated liver and gallbladder profiles with observations.

RESULTS

The established hepatobiliary model well captured the kinetic profiles of rosuvastatin in the liver and gallbladder during the PET scans. Application of dual input function led to a marked underestimation of liver concentrations at the initial stage after i.v. dosing which cannot be offset by altering model parameter values. The simulated hepatobiliary profiles from three of the reported models demonstrated substantial deviation from the observed data.

CONCLUSIONS

The present study highlights the necessity of using hepatobiliary data to verify and improve the predictive performance of hepatic disposition of rosuvastatin.

Crosstalk of physiological pH and chemical pKa under the umbrella of physiologically based pharmacokinetic modeling of drug absorption, distribution, metabolism, excretion, and toxicity

Introduction: Physiological pH and chemical pKa are two sides of the same coin in defining the ionization of a drug in the human body. The Henderson-Hasselbalch equation and pH-partition hypothesis form the theoretical base to define the impact of pH-pKa crosstalk on drug ionization and thence its absorption, distribution, metabolism, excretion, and toxicity (ADMET).Areas covered: Human physiological pH is not constant, but a diverse, dynamic state regulated by various biological mechanisms, while the chemical pKa is generally a constant defining the acidic dissociation of the drug at various environmental pH. Works on pH-pKa crosstalk are scattered in the literature, despite its significant contributions to drug pharmacokinetics, pharmacodynamics, safety, and toxicity. In particular, its impacts on drug ADMET have not been effectively linked to the physiologically based pharmacokinetic (PBPK) modeling and simulation, a powerful tool increasingly used in model-informed drug development (MIDD).Expert opinion: Lacking a full consideration of the interactions of physiological pH and chemical pKa in a PBPK model limits scientists' capability in mechanistically describing the drug ADMET. This mini-review compiled literature knowledge on pH-pKa crosstalk and its impacts on drug ADMET, from the viewpoint of PBPK modeling, to pave the way to a systematic incorporation of pH-pKa crosstalk into PBPK modeling and simulation.

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.

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.

Comparative Hepatic and Intestinal Metabolism and Pharmacodynamics of Statins

This study aimed to comprehensively investigate the in vitro metabolism of statins. The metabolism of clinically relevant concentrations of atorvastatin, fluvastatin, pitavastatin, pravastatin, rosuvastatin, simvastatin, and their metabolites were investigated using human liver microsomes (HLMs), human intestine microsomes (HIMs), liver cytosol, and recombinant cytochrome P450 enzymes. We also determined the inhibitory effects of statin acids on their pharmacological target, 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase. In HLMs, statin lactones were metabolized to a much higher extent than their acid forms. Atorvastatin lactone and simvastatin (lactone) showed extensive metabolism [intrinsic clearance (CL_int) values of 3700 and 7400 µl/min per milligram], whereas the metabolism of the lactones of 2-hydroxyatorvastatin, 4-hydroxyatorvastatin, and pitavastatin was slower (CL_int 20-840 µl/min per milligram). The acids had CL_int values in the range <0.1-80 µl/min per milligram. In HIMs, only atorvastatin lactone and simvastatin (lactone) exhibited notable metabolism, with CL_int values corresponding to 20% of those observed in HLMs. CYP3A4/5 and CYP2C9 were the main statin-metabolizing enzymes. The majority of the acids inhibited HMG-CoA reductase, with 50% inhibitory concentrations of 4-20 nM. The present comparison of the metabolism and pharmacodynamics of the various statins using identical methods provides a strong basis for further application, e.g., comparative systems pharmacology modeling. SIGNIFICANCE STATEMENT: The present comparison of the in vitro metabolic and pharmacodynamic properties of atorvastatin, fluvastatin, pitavastatin, pravastatin, rosuvastatin, and simvastatin and their metabolites using unified methodology provides a strong basis for further application. Together with in vitro drug transporter and clinical data, the present findings are applicable for use in comparative systems pharmacology modeling to predict the pharmacokinetics and pharmacological effects of statins at different dosages.

Recent developments in in vitro and in vivo models for improved translation of preclinical pharmacokinetics and pharmacodynamics data

Improved pharmacokinetics/pharmacodynamics (PK/PD) prediction in the early stages of drug development is essential to inform lead optimization strategies and reduce attrition rates. Recently, there have been significant advancements in the development of new in vitro and in vivo strategies to better characterize pharmacokinetic properties and efficacy of drug leads. Herein, we review advances in experimental and mathematical models for clearance predictions, advancements in developing novel tools to capture slowly metabolized drugs, in vivo model developments to capture human etiology for supporting drug development, limitations and gaps in these efforts, and a perspective on the future in the field.

Overcoming the shortcomings of the extended-clearance concept: a framework for developing a physiologically-based pharmacokinetic (PBPK) model to select drug candidates involving transporter-mediated clearance

Introduction:Human pharmacokinetic (PK) prediction can be a significant challenge to drug candidates undergoing transporter-mediated clearance, when only animal data and in vitro human parameters are available in the drug discovery stage.Areas covered:The extended clearance concept (ECC) that incorporates the processes of hepatic uptake, passive diffusion, metabolism and biliary secretion has been adapted to determine the rate-determining process of hepatic clearance and drug-drug interactions (DDIs). However, since the ECC is derived from the well-stirred model and does not consider the liver as a drug distribution organ to reflect the time-dependent variation of drug concentrations between the liver and plasma, it can be misused for compound selection in drug discovery.Expert opinion:The PBPK model consists of a set of differential equations of drug mass balance, and can overcome the shortcomings of the ECC in predicting human PK. The predictability, relevance and reliability of the model and the scaling factors for IVIVE must be validated using either the measured liver concentrations or DDI data with known transporter inhibitors, or both, in monkeys. A human PBPK model that incorporates in vitro human data and SFs obtained from the validated monkey PBPK model can be used for compound selection in the drug discovery phase.

Organic Anion-Transporting Polypeptide 1B1/1B3-Mediated Hepatic Uptake Determines the Pharmacokinetics of Large Lipophilic Acids: In Vitro-In Vivo Evaluation in Cynomolgus Monkey

It is generally presumed that uptake transport mechanisms are of limited significance in hepatic clearance for lipophilic or high passive-permeability drugs. In this study, we evaluated the mechanistic role of the hepato-selective organic anion-transporting polypeptides (OATPs) 1B1/1B3 in the pharmacokinetics of compounds representing large lipophilic acid space. Intravenous pharmacokinetics of 16 compounds with molecular mass ∼400-730 Da, logP ∼3.5-8, and acid pKa <6 were obtained in cynomolgus monkey after dosing without and with a single-dose rifampicin-OATP1B1/1B3 probe inhibitor. Rifampicin (30 mg/kg oral) significantly (P < 0.05) reduced monkey clearance and/or steady-state volume of distribution (VDss) for 15 of 16 acids evaluated. Additionally, clearance of danoprevir was reduced by about 35%, although statistical significance was not reached. A significant linear relationship was noted between the clearance ratio (i.e., ratio of control to treatment groups) and VDss ratio, suggesting hepatic uptake contributes to the systemic clearance and distribution simultaneously. In vitro transport studies using primary monkey and human hepatocytes showed uptake inhibition by rifampicin (100 µM) for compounds with logP ≤6.5 but not for the very lipophilic acids (logP > 6.5), which generally showed high nonspecific binding in hepatocyte incubations. In vitro uptake clearance and fraction transported by OATP1B1/1B3 (f_t,OATP1B) were found to be similar in monkey and human hepatocytes. Finally, for compounds with logP ≤6.5, good agreement was noted between in vitro f_t,OATP1B and clearance ratio (as well as VDss ratio) in cynomolgus monkey. In conclusion, this study provides mechanistic evidence for the pivotal role of OATP1B-mediated hepatic uptake in the pharmacokinetics across a wide, large lipophilic acid space. SIGNIFICANCE STATEMENT: This study provides mechanistic insight into the pharmacokinetics of a broad range of large lipophilic acids. Organic anion-transporting polypeptides 1B1/1B3-mediated hepatic uptake is of key importance in the pharmacokinetics and drug-drug interactions of almost all drugs and new molecular entities in this space. Diligent in vitro and in vivo transport characterization is needed to avoid the false negatives often noted because of general limitations in the in vitro assays while handling compounds with such physicochemical attributes.

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

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

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.

Assessing OATP1B1- and OATP1B3-Mediated Drug-Drug Interaction Potential of Vemurafenib Using R-Value and Physiologically-Based Pharmacokinetic Models

Organic anion transporting polypeptides (OATP) 1B1 and OATP1B3 are important determinants of transporter-mediated drug-drug interactions (DDIs). Current studies assessed the OATP1B1 and OATP1B3-mediated DDI potential of vemurafenib, a kinase inhibitor drug with high protein binding and low aqueous solubility, using R-value and physiologically-based pharmacokinetic (PBPK) models. The total half-maximal inhibitory concentration (IC50,total) values of vemurafenib against OATP1B1 and OATP1B3 were determined in 100% human plasma in transporter-overexpressing human embryonic kidney 293 stable cell lines. The unbound fraction of vemurafenib in human plasma before (fu,plasma) and after addition into the uptake assay plate (fu,plasma,inc) were determined by rapid equilibrium dialysis. There was no statistically significant difference between fu,plasma and fu,plasma,inc. Vemurafenib IC50,total values against OATP1B1 and OATP1B3 are 175 ± 82 and 231 ± 26 μM, respectively. The R-values [R = 1 + fu,plasma × Iin,max/(fu,plasma,inc × IC50,total)] were then simplified as R = 1+Iin,max/IC50,total, and were 1.76 and 1.57 for OATP1B1 and OATP1B3, respectively. The simulated pravastatin AUC ratio was 1.28 when a single dose of pravastatin (40 mg) was co-administered with vemurafenib (960 mg, twice daily) at steady-state, compared to pravastatin alone. Both R-value and PBPK models predict that vemurafenib has the potential to cause OATP1B1- and OATP1B3-mediated DDIs.

Evaluation of Hepatic Uptake of OATP1B Substrates by Short Term-Cultured Plated Human Hepatocytes: Comparison With Isolated Suspended Hepatocytes

Hepatic uptake clearance has been measured in suspended human hepatocytes (SHH). Plated human hepatocytes (PHH) after short-term culturing are increasingly employed to study hepatic transport driven mainly by its higher throughput. To know pros/cons of both systems, the hepatic uptake clearances of several organic anion transporting polypeptide 1B substrates were compared between PHH and SHH by determining the initial uptake velocities or through dynamic model-based analyses. For cerivastatin, pitavastatin and rosuvastatin, initial uptake clearances (PS_inf) obtained using PHH were comparable to those using SHH, while cell-to-medium concentration (C/M) ratios were 2.7- to 5.4-fold higher. For pravastatin and dehydropravastatin, hydrophilic compounds with low uptake/cellular binding, their PS_inf and C/M ratio in PHH were 1.8- to 3.2-fold lower than those in SHH. These hydrophilic substrates are more prone to wash-off during the uptake study using PHH, which may explain the apparently lower uptake than SHH. The C/M ratios obtained using PHH were stable over an extended time, making PHH suitable to estimate the C/M ratios and hepatocyte-to-medium unbound concentration ratios (K_p,uu). In conclusion, PHH is useful in evaluating hepatic uptake/efflux clearances and K_p,uu of OATP1B substrates in a high-throughput manner, however, a caution is warranted for hydrophilic drugs with low uptake/cellular binding.

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.

In Vitro Hepatic Uptake in Human and Monkey Hepatocytes in the Presence and Absence of Serum Protein and Its In Vitro to In Vivo Extrapolation

It is well documented that human hepatic clearance based on in vitro metabolism or transporter assays systematically resulted in underprediction; therefore, large empirical scalars are often needed in either static or physiologically based pharmacokinetic (PBPK) models to accurately predict human pharmacokinetics (PK). In our current investigation, we assessed hepatic uptake in hepatocyte suspension in Krebs-Henseleit buffer in the presence and absence of serum. The results showed that the unbound intrinsic active clearance (CL_u,int,active) values obtained by normalizing the unbound fraction in the buffer containing 10% serum were generally higher than the CL_u,int,active obtained directly from protein free buffer, suggesting "protein-facilitated" uptake. The differences of CL_u,int,active in the buffer with and without protein ranged from 1- to 925-fold and negatively correlated to the unbound serum binding of organic anion transporting polypeptide substrates. When using the uptake values obtained from buffer containing serum versus serum-free buffer, the median of scaling factors (SFs) for CL_u,int,active reduced from 24.2-4.6 to 22.7-7.1 for human and monkey, respectively, demonstrating the improvement of in vitro to in vivo extrapolation in a PBPK model. Furthermore, values of CL_u,int,active were significantly higher in monkey hepatocytes than that in human, and the species differences appeared to be compound dependent. Scaling up in vitro uptake values derived in assays containing species-specific serum can compensate for the species-specific variabilities when using cynomolgus monkey as a probe animal model. Incorporating SFs calibrated in monkey and together with scaled in vitro data can be a reliable approach for the prospective human PK prediction in early drug discovery. SIGNIFICANCE STATEMENT: We investigated the protein effect on hepatic uptake in human and monkey hepatocytes and improved the in vitro to in vivo extrapolation using parameters obtained from the incubation in the present of serum protein. In addition, significantly higher active uptake clearances were observed in monkey hepatocytes than in human, and the species differences appeared to be compound dependent. The physiologically based pharmacokinetic model that incorporates scaling factors calibrated in monkey and together with scaled in vitro human data can be a reliable approach for the prospective human pharmacokinetics prediction.

A 3D brain unit model to further improve prediction of local drug distribution within the brain

The development of drugs targeting the brain still faces a high failure rate. One of the reasons is a lack of quantitative understanding of the complex processes that govern the pharmacokinetics (PK) of a drug within the brain. While a number of models on drug distribution into and within the brain is available, none of these addresses the combination of factors that affect local drug concentrations in brain extracellular fluid (brain ECF). Here, we develop a 3D brain unit model, which builds on our previous proof-of-concept 2D brain unit model, to understand the factors that govern local unbound and bound drug PK within the brain. The 3D brain unit is a cube, in which the brain capillaries surround the brain ECF. Drug concentration-time profiles are described in both a blood-plasma-domain and a brain-ECF-domain by a set of differential equations. The model includes descriptions of blood plasma PK, transport through the blood-brain barrier (BBB), by passive transport via paracellular and transcellular routes, and by active transport, and drug binding kinetics. The impact of all these factors on ultimate local brain ECF unbound and bound drug concentrations is assessed. In this article we show that all the above mentioned factors affect brain ECF PK in an interdependent manner. This indicates that for a quantitative understanding of local drug concentrations within the brain ECF, interdependencies of all transport and binding processes should be understood. To that end, the 3D brain unit model is an excellent tool, and can be used to build a larger network of 3D brain units, in which the properties for each unit can be defined independently to reflect local differences in characteristics of the brain.

Comparison of In Vitro to In Vivo Extrapolation Approaches for Predicting Transporter-Mediated Hepatic Uptake Clearance Using Suspended Rat Hepatocytes

Clearance (CL) prediction remains a significant challenge in drug discovery, especially when complex processes such as drug transporters are involved. The present work explores various in vitro to in vivo extrapolation (IVIVE) approaches to predict hepatic CL driven by uptake transporters in rat. Broadly, two different IVIVE methods using suspended rat hepatocytes were compared: initial uptake CL (PS_u,inf) and intrinsic metabolic CL (CL_int,met) corrected by unbound hepatocytes to medium partition coefficient (Kp_uu). Kp_uu was determined by temperature method (Temp Kp_uu,ss), homogenization method (Hom Kp_uu,ss), and initial rate method (Kp_uu,V0). In addition, the impact of bovine serum albumin (BSA) on each of these methods was investigated. Twelve compounds, which are known substrates of organic anion-transporting polypeptides representing diverse chemical matter, were selected for these studies. As expected, CL_int,met alone significantly underestimated hepatic CL for all the test compounds. Overall, predicted hepatic CL using PS_u,inf with BSA, Hom Kp_uu,ss with BSA, and Temp Kp_uu,ss showed the most robust correlation with in vivo rat hepatic CL. Adding BSA improved hepatic CL prediction for selected compounds when using the PS_u,inf and Hom Kp_uu,ss methods, with minimal impact on the Temp Kp_uu,ss and Kp_uu,V0 methods. None of the IVIVE approaches required an empirical scaling factor. These results suggest that supplementing rat hepatocyte suspension with BSA may be essential in drug discovery research for novel chemical matters to improve CL prediction. SIGNIFICANCE STATEMENT: The current investigation demonstrates that hepatocyte uptake assay supplemented with 4% bovine serum albumin is a valuable tool for estimating unbound hepatic uptake clearance (CL) and Kp_uu. Based upon the extended clearance concept, direct extrapolation from these in vitro parameters significantly improved the overall hepatic CL prediction for organic anion-transporting polypeptide substrates in rat. This study provides a practical in vitro to in vivo extrapolation strategy for predicting transporter-mediated hepatic CL in early drug discovery.

Interpretation of Drug Interaction Using Systemic and Local Tissue Exposure Changes

Systemic exposure of a drug is generally associated with its pharmacodynamic (PD) effect (e.g., efficacy and toxicity). In this regard, the change in area under the plasma concentration-time curve (AUC) of a drug, representing its systemic exposure, has been mainly considered in evaluation of drug-drug interactions (DDIs). Besides the systemic exposure, the drug concentration in the tissues has emerged as a factor to alter the PD effects. In this review, the status of systemic exposure, and/or tissue exposure changes in DDIs, were discussed based on the recent reports dealing with transporters and/or metabolic enzymes mediating DDIs. Particularly, the tissue concentration in the intestine, liver and kidney were referred to as important factors of PK-based DDIs.

Use of imaging to assess the activity of hepatic transporters

Introduction: Membrane transporters of the SLC and ABC families are abundantly expressed in the liver, where they control the transfer of drugs/drug metabolites across the sinusoidal and canalicular hepatocyte membranes and play a pivotal role in hepatic drug clearance. Noninvasive imaging methods, such as PET, SPECT or MRI, allow for measuring the activity of hepatic transporters in vivo, provided that suitable transporter imaging probes are available.Areas covered: We give an overview of the working principles of imaging-based assessment of hepatic transporter activity. We discuss different currently available PET/SPECT radiotracers and MRI contrast agents and their applications to measure hepatic transporter activity in health and disease. We cover mathematical modeling approaches to obtain quantitative parameters of transporter activity and provide a critical assessment of methodological limitations and challenges associated with this approach.Expert opinion: PET in combination with pharmacokinetic modeling can be potentially applied in drug development to study the distribution of new drug candidates to the liver and their clearance mechanisms. This approach bears potential to mechanistically assess transporter-mediated drug-drug interactions, to assess the influence of disease on hepatic drug disposition and to validate and refine currently available in vitro-in vivo extrapolation methods to predict hepatic clearance of drugs.

Leflunomide increased the renal exposure of acyclovir by inhibiting OAT1/3 and MRP2

Rheumatoid arthritis patients can be prescribed a combination of immunosuppressive drug leflunomide (LEF) and the antiviral drug acyclovir to reduce the high risk of infection. Acyclovir is a substrate of organic anion transporter (OAT) 1/3 and multidrug resistance-associated protein (MRP) 2. Considering the extraordinarily long half-life of LEF's active metabolite teriflunomide (TER) and the kidney injury risk of acyclovir, it is necessary to elucidate the potential impact of LEF on the disposition of acyclovir. Here we used a specific MRP inhibitor MK571 and probenecid (OAT1/3 and MRP2 inhibitor) to assess the effects of MRP2 and OAT1/3 on the pharmacokinetics and tissue distribution of acyclovir in rats. We showed that LEF and probenecid, but not MK571 significantly increased the plasma concentration of acyclovir. However, kidney and liver exposures of acyclovir were increased when coadministered with LEF, probenecid or MK571. The kidney/plasma ratio of acyclovir was increased to approximately 2-fold by LEF or probenecid, whereas it was increased to as much as 14.5-fold by MK571. Consistently, these drugs markedly decreased the urinary excretion of acyclovir. TER (0.5-100 μmol/L) dose-dependently increased the accumulation of acyclovir in MRP2-MDCK cells with an IC₅₀ value of 4.91 μmol/L. TER (5 μmol/L) significantly inhibited the uptake of acyclovir in hOAT1/3-HEK293 cells. These results suggest that LEF/TER increased the kidney accumulation of acyclovir by inhibiting the efflux transporter MRP2, which increased its kidney/plasma ratio and renal injury risk. However, the inhibitory effects of LEF/TER on OAT1/3 reduced the tubular cells' uptake of acyclovir and increased the plasma concentration.

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.

Prediction of Metabolite-to-Parent Drug Exposure: Derivation and Application of a Mechanistic Static Model

In the development of new drugs, the prediction of metabolite‐to‐parent plasma exposure ratio in humans prior to administration in a clinical study has emerged as an important need. In this work, we derived a mechanistic static model based on first principles to estimate metabolite‐to‐parent plasma exposure ratio, considering the contribution of liver and gut metabolism and drug transport. Knowledge (or assumptions) of mechanisms of clearance and organs involved is required. Input parameters needed included intrinsic clearance, fraction of clearance to the metabolite of interest, various binding values, and, in some cases, active transport clearance. The principles are illustrated with four drugs that yield six metabolites, with one in which clearance is dependent on a pathway subject to genetic polymorphism. Overall, the approach yielded metabolite‐to‐parent ratios within about twofold of the actual values and, thus, can be valuable in decision making in the drug development process.