Accounting for Transporters in Renal Clearance: Towards a Mechanistic Kidney Model (Mech KiM)

Application of a Physiologically Based Pharmacokinetic Approach to Predict Tenofovir Pharmacokinetics During Pregnancy

Pharmacotherapy during pregnancy requires a better understanding of the impact of changes in maternal physiology on the maternal and fetal drug exposure. The physiologically based pharmacokinetic (PBPK) modelling approach can be applied to predict maternal and fetal exposure. In vitro and in vivo PK data in non-pregnant individuals were compiled and used to develop and verify a PBPK model for tenofovir. The model was then used to predict the maternal and fetal tenofovir exposure during pregnancy, after incorporation of current knowledge on maternal and fetal physiological changes during pregnancy. Predicted concentrations and parameters from the PBPK model were compared to observed data. Predicted tenofovir PK agreed with observations in non-pregnant (13 studies) and pregnant (7 studies with differing gestational weeks) individuals. Observed concentrations fell within the PBPK 5th - 95th prediction intervals. Predicted PK parameters were within twofold of the reported parameters. The predicted tenofovir steady state cord-to-maternal exposure ratio at term was 0.85 (range: 0.62-0.98), which agrees with clinically observed ratios ranging between 0.60-1.00. A PBPK model for tenofovir was constructed and used to simulate the maternal and fetal exposure to tenofovir in virtual pregnant women population at different gestational weeks. Applying a similar approach to other drugs or chemicals may allow exposure prediction and risk assessment in the fetus following maternal administration of drugs or unintended exposure to environmental toxicants.

Is the GFR-based scaling approach adequate for predicting pediatric renal clearance of drugs with passive tubular reabsorption? Insights from PBPK modeling

Empirical maturation models (e.g., Johnson and Rhodin models) for glomerular filtration rate (GFR) are commonly used as scaling factors for predicting pediatric renal clearance, but their predictive performance for drugs featured with tubular reabsorption is poorly understood. This study investigated the adequacy of GFR-based scaling models for predicting pediatric renal clearance in drugs with passive tubular reabsorption by comparing with a mechanistic kidney model (Mech-KiM) that encompasses the physiological processes of glomerular filtration, tubular secretion, and reabsorption. The analysis utilized hypothetical drugs with varying fractions of tubular reabsorption (Freabs), alongside the model drug metronidazole, which has a Freabs of 96%. Our simulations showed that when Freabs is ≤70%, the discrepancies between the GFR-based scaling methods and the Mech-KiM model in predicting pediatric renal clearance were generally within a twofold range throughout childhood. However, for drugs with substantial tubular reabsorption (e.g., Freabs > 70%), discrepancies greater than twofold were observed between the GFR-based scaling methods and the Mech-KiM model in predicting renal clearance for young children. In neonates, the differences ranged from 5- to 10-fold when the adult Freabs was 95%. Pediatric physiologically based pharmacokinetic (PBPK) modeling of metronidazole revealed that using a GFR-based scaling method (Johnson model) significantly overestimated drug concentrations in children under 2 months, whereas utilizing the Mech-KiM model for renal clearance predictions yielded estimates closely aligned with observed concentrations. Our study demonstrates that using GFR-based scaling models to predict pediatric renal clearance might be inadequate for drugs with extensive passive tubular reabsorption (e.g., Freabs > 70%).

How PBPK Can Help to Understand Old Drugs and Inform their Dosing in Elderly: Amantadine Case Study

Amantadine, despite being on the market for 55 years, has several unknown aspects of its pharmacokinetics especially related to the influence of covariates such as age, disease, or interactions linked to amantadine's renal elimination. As amantadine is used in Parkinson's disease and is considered a potential candidate in COVID treatment and other diseases, there is an unmet need for thorough understanding of its pharmacokinetic in special populations, such as the elderly. We aimed to mechanistically describe amantadine pharmacokinetics in healthy subjects and shed some light on the differences in drug behavior between healthy volunteers (18-65 years) and an elderly/geriatric population (65-98 years) using PBPK modeling and simulation. The middle-out PBPK model includes mechanistic description of drug renal elimination, specifically an organic cation transporter (OCT)2-mediated electrogenic bidirectional transport (basolateral) and multidrug and toxic compound extrusion (MATE)1-mediated efflux (apical). The model performance was verified against plasma and urine data reported after single and multiple dose administration in healthy volunteers and elderly patients from 18 independent studies. The ratios of predicted vs. observed maximal plasma concentration and area under the concentration-time curve values were within 1.25-fold. The model illustrates that renal transporter activity is expected to decrease in healthy elderly compared to healthy volunteers, which is in line with literature proteomic data for OCT2. The model was applied to assess the potential of reaching toxicity-related plasma concentrations in different age groups of geriatric subjects.

Prediction of the Renal Organic Anion Transporter 1 (OAT1)- Mediated Drug Interactions for LY404039, the Active Metabolite of Pomaglumetad Methionil

PURPOSE

The objective of this work was to demonstrate that clinical OAT1-mediated DDIs can be predicted using physiologically based pharmacokinetic (PBPK) modeling.

METHODS

LY404039 is a metabotropic glutamate receptor 2/3 agonist and the active moiety of the prodrug pomaglumetad methionil (LY2140023). After oral administration, pomaglumetad methionil is rapidly taken up by enterocytes via PEPT1 and once absorbed, converted to LY404039 via membrane dehydropeptidase 1 (DPEP1). LY404039 is renally excreted by both glomerular filtration and active secretion and in vitro studies showed that the active secretion of LY404039 was mediated by the organic anion transporter 1 (OAT1). Both clinical and in vitro data were used to build a PBPK model to predict OAT1-mediated DDIs.

RESULTS

In vitro inhibitory potencies (IC50) of the known OAT inhibitors, probenecid and ibuprofen, were determined to be 4.00 and 2.63 µM, respectively. Subsequently, clinical drug-drug interaction (DDI) study showed probenecid reduced the renal clearance of LY404039 by 30 to 40%. The PBPK bottom-up model, predicted a renal clearance that was approximately 20% lower than the observed one. The middle-out model, using an OAT1 relative activity factor (RAF) of 3, accurately reproduced the renal clearance of LY404039 and pharmacokinetic (PK) changes of LY404039 in the presence of probenecid.

CONCLUSIONS

OAT1- mediated DDIs can be predicted using in vitro measured IC50 and PBPK modeling. The effect of ibuprofen was predicted to be minimal (AUC ratio of 1.15) and not clinically relevant.

Important roles of transporters in the pharmacokinetics of anti-viral nucleoside/nucleotide analogs

INTRODUCTION

Nucleoside analogs are an important class of antiviral agents. Due to the high hydrophilicity and limited membrane permeability of antiviral nucleoside/nucleotide analogs (AVNAs), transporters play critical roles in AVNA pharmacokinetics. Understanding the properties of these transporters is important to accelerate translational research for AVNAs.

AREAS COVERED

The roles of key transporters in the pharmacokinetics of 25 approved AVNAs were reviewed. Clinically relevant information that can be explained by the modulation of transporter functions is also highlighted.

EXPERT OPINION

Although the roles of transporters in the intestinal absorption and renal excretion of AVNAs have been well identified, more research is warranted to understand their roles in the distribution of AVNAs, especially to immune privileged compartments where treatment of viral infection is challenging. P-gp, MRP4, BCRP, and nucleoside transporters have shown extensive impacts in the disposition of AVNAs. It is highly recommended that the role of transporters should be investigated during the development of novel AVNAs. Clinically, co-administered inhibitors and genetic polymorphism of transporters are the two most frequently reported factors altering AVNA pharmacokinetics. Physiopathology conditions also regulate transporter activities, while their effects on pharmacokinetics need further exploration. Pharmacokinetic models could be useful for elucidating these complicated factors in clinical settings.

Development and application of a pediatric mechanistic kidney model

Pediatric physiologically‐based pharmacokinetic (P‐PBPK) models have been used to predict age related changes in the pharmacokinetics (PKs) of renally cleared drugs mainly in relation to changes in glomerular filtration rate. With emerging data on ontogeny of renal transporters, mechanistic models of renal clearance accounting for the role of active and passive secretion should be developed and evaluated. Data on age‐related physiological changes and ontogeny of renal transporters were applied into a mechanistic kidney within a P‐PBPK model. Plasma concentration–time profile and PK parameters of cimetidine, ciprofloxacin, metformin, tenofovir, and zidovudine were predicted in subjects aged 1 day to 18 years. The predicted and observed plasma concentration–time profiles and PK parameters were compared. The predicted concentration–time profile means and 5th and 95th percent intervals generally captured the observed data and variability in various studies. Overall, based on drugs and age bands, predicted to observed clearance were all within two‐fold and in 11 of 16 cases within 1.5‐fold. Predicted to observed area under the curve (AUC) and maximum plasma concentration (C_max) were within two‐fold in 12 of 14 and 12 of 15 cases, respectively. Predictions in neonates and early infants (up to 14 weeks postnatal age) were reasonable with 15–20 predicted PK parameters within two‐fold of the observed. ciprofloxacin but not zidovudine PK predictions were sensitive to basal kidney uptake transporter ontogeny. The results indicate that a mechanistic kidney model accounting for physiology and ontogeny of renal processes and transporters can predict the PK of renally excreted drugs in children. Further data especially in neonates are required to verify the model and ontogeny profiles.

Prediction of Maternal and Fetoplacental Concentrations of Cefazolin, Cefuroxime, and Amoxicillin during Pregnancy Using Bottom-Up Physiologically Based Pharmacokinetic Models

Concerns over maternal and fetal drug exposures highlight the need for a better understanding of drug distribution into the fetus through the placental barrier. This study aimed to predict maternal and fetal drug disposition using physiologically based pharmacokinetic (PBPK) modeling. The detailed maternal-placental-fetal PBPK model within the Simcyp Simulator V20 was used to predict the maternal and fetoplacental exposure of cefazolin, cefuroxime, and amoxicillin during pregnancy and at delivery. The mechanistic dynamic model includes physiologic changes of the maternal, fetal, and placental parameters over the course of pregnancy. Placental kinetics were parametrized using permeability parameters determined from the physicochemical properties of these compounds. Then, the PBPK predictions were compared with the observed data. Fully bottom-up fetoplacental PBPK models were developed for cefuroxime, cefazolin, and amoxicillin without any parameter fitting. Predictions in nonpregnant subjects and in pregnant subjects fall within 2-fold of the observed values. Predictions matched observed pharmacokinetic data reported in nine maternal (five fetoplacental) studies for cefuroxime, 10 maternal (five fetoplacental) studies for cefazolin, and six maternal (two fetoplacental) studies for amoxicillin. Integration of the fetal and maternal system parameters within PBPK models, together with compound-related parameters used to calculate placental permeability, facilitates and extends the applications of the maternal-placental-fetal PBPK model. The developed model can also be used for designing clinical trials and prospectively used for maternal-fetal risk assessment after maternally administered drugs or unintended exposure to environmental toxicants. SIGNIFICANCE STATEMENT: This study investigates the performance of an integrated maternal-placental-fetal PBPK model to predict maternal and fetal tissue exposure of renally eliminated antibiotics that cross the placenta through a passive diffusion mechanism. The transplacental permeability clearance was predicted from the drug physicochemical properties. Results demonstrate that the PBPK approach can facilitate the prediction of maternal and fetal drug exposure simultaneously at any gestational age to support its use in the maternal-fetal exposure assessments.

PBPK Simulation-Based Evaluation of Ganciclovir Crystalluria Risk Factors: Effect of Renal Impairment, Old Age, and Low Fluid Intake

Dosing guidance is often lacking for chronic kidney disease (CKD) due to exclusion of such patients from pivotal clinical trials. Physiologically based pharmacokinetic (PBPK) modelling supports model-informed dosing when clinical data are lacking, but application of these approaches to patients with impaired renal function is not yet at full maturity. In the current study, a ganciclovir PBPK model was developed for patients with normal renal function and extended to CKD population. CKD-related changes in tubular secretion were explored in the mechanistic kidney model and implemented either as proportional or non-proportional decline relative to GFR. Crystalluria risk was evaluated in different clinical settings (old age, severe CKD and low fluid intake) by simulating ganciclovir medullary collecting duct (MCD) concentrations. The ganciclovir PBPK model captured observed changes in systemic pharmacokinetic endpoints in mild-to-severe CKD; these trends were evident irrespective of assumed pathophysiological mechanism of altered active tubular secretion in the model. Minimal difference in simulated ganciclovir MCD concentrations was noted between young adult and geriatric populations with normal renal function and urine flow (1 mL/min), with lower concentrations predicted for severe CKD patients. High crystalluria risk was identified at reduced urine flow (0.1 mL/min) as simulated ganciclovir MCD concentrations exceeded its solubility (2.6–6 mg/mL), irrespective of underlying renal function. The analysis highlighted the importance of appropriate distribution of virtual subjects’ systems data in CKD populations. The ganciclovir PBPK model illustrates the ability of this translational tool to explore individual and combined effects of age, urine flow, and renal impairment on local drug renal exposure.

Quantitation of Plasma Membrane Drug Transporters in Kidney Tissue and Cell Lines Using a Novel Proteomic Approach Enabled a Prospective Prediction of Metformin Disposition

The successful prospective incorporation of in vitro transporter kinetics in physiologically based pharmacokinetic (PBPK) models to describe drug disposition remains challenging. Although determination of scaling factors to extrapolate in vitro to in vivo transporter kinetics has been facilitated by quantitative proteomics, no robust assessment comparing membrane recoveries between different cells/tissues has been made. HEK293 cells overexpressing OCT2, MATE1, and MATE2K or human kidney cortex were homogenized and centrifuged to obtain the total membrane fractions, which were subsequently subjected to liquid-liquid extraction followed by centrifugation and precipitation to isolate plasma membrane fractions. Plasma membrane recoveries determined by quantitation of the marker Na+/K+-ATPase in lysate and plasma membrane fractions were ≤20% but within 3-fold across different cells and tissues. A separate study demonstrated that recoveries are comparable between basolateral and apical membranes of renal proximal tubules, as measured by Na+/K+-ATPase and γ-glutamyl transpeptidase 1, respectively. The plasma membrane expression of OCT2, MATE1, and MATE2K was quantified and relative expression factors (REFs) were determined as the ratio between the tissue and cell concentrations. Corrections using plasma membrane recovery had minimal impact on REF values (<2-fold). In vitro transporter kinetics of metformin were extrapolated to in vivo using the corresponding REFs in a PBPK model. The simulated metformin exposures were within 2-fold of clinical exposure. These results demonstrate that transporter REFs based on plasma membrane expression enable a prediction of transporter-mediated drug disposition. Such REFs may be estimated without the correction of plasma membrane recovery when the same procedure is applied between different matrices. SIGNIFICANCE STATEMENT: Transporter REFs based on plasma membrane expression enable in vitro-in vivo extrapolation of transporter kinetics. Plasma membrane recoveries as determined by the quantification of sodium-potassium adenosine triphosphatase were comparable between the in vitro and in vivo systems used in the present study, and therefore had minimal impact on the transporter REF values.

Para-aminosalicylic acid significantly reduced tenofovir exposure in human subjects: Mismatched findings from in vitro to in vivo translational research

AIMS

Tenofovir and para-aminosalicylic acid (PAS) may be coprescribed to treat patients with concomitant infections of human immunodeficiency virus and Mycobacterium tuberculosis bacteria. Both drugs are known to have remarkable renal uptake transporter-mediated clearance. Owing to the lack of clinical studies on drug-drug interaction between the 2 drugs, we conducted a translational clinical study to investigate the effect of PAS on tenofovir pharmacokinetics (PK).

METHODS

Initially, we studied in vitro renal uptake transporter-mediated drug-drug interactions using stably transfected cells with human organic anion transporters (OAT1 and OAT3). Later, we estimated clinical drug interactions using static and physiologically based PK modelling. Finally, we investigated the effects of PAS-calcium formulation (PAS-Ca) on tenofovir disoproxil fumarate PK in healthy male Korean subjects.

RESULTS

PAS inhibited OAT1- and OAT3-mediated tenofovir uptake in vitro. The physiologically based PK drug-drug interaction model suggested a 1.26-fold increase in tenofovir peak plasma concentration when coadministered with PAS. By contrast, an open-label, randomized, crossover clinical trial evaluating the effects of PAS-Ca on tenofovir PK showed significantly altered geometric mean ratio (90% confidence intervals) of maximum plasma concentration (C_max ) and area under the curve (AUC_0-inf ) by 0.33 (0.28-0.38) and 0.29 (0.26-0.33), respectively.

CONCLUSION

Our study findings suggest that the PAS-Ca formulation significantly reduced systemic exposure to tenofovir through an unexplained mechanism, which was contrary to the initial prediction. Caution should be exercised while predicting in vivo PK profiles from in vitro data, particularly when there are potential confounders such as pharmaceutical interactions.

Quantitative Proteomic Map of Enzymes and Transporters in the Human Kidney: Stepping Closer to Mechanistic Kidney Models to Define Local Kinetics

The applications of translational modeling of local drug concentrations in various organs had a sharp increase over the last decade. These are part of the model-informed drug development initiative, adopted by the pharmaceutical industry and promoted by drug regulatory agencies. With respect to the kidney, the models serve as a bridge for understanding animal vs. human observations related to renal drug disposition and any consequential adverse effects. However, quantitative data on key drug-metabolizing enzymes and transporters relevant for predicting renal drug disposition are limited. Using targeted and global quantitative proteomics, we determined the abundance of multiple enzymes and transporters in 20 human kidney cortex samples. Nine enzymes and 22 transporters were quantified (8 for the first time in the kidneys). In addition, > 4,000 proteins were identified and used to form an open database. CYP2B6, CYP3A5, and CYP4F2 showed comparable, but generally low expression, whereas UGT1A9 and UGT2B7 levels were the highest. Significant correlation between abundance and activity (measured by mycophenolic acid clearance) was observed for UGT1A9 (Rs = 0.65, P = 0.004) and UGT2B7 (Rs = 0.70, P = 0.023). Expression of P-gp ≈ MATE-1 and OATP4C1 transporters were high. Strong intercorrelations were observed between several transporters (P-gp/MRP4, MRP2/OAT3, and OAT3/OAT4); no correlation in expression was apparent for functionally related transporters (OCT2/MATEs). This study extends our knowledge of pharmacologically relevant proteins in the kidney cortex, with implications on more prudent use of mechanistic kidney models under the general framework of quantitative systems pharmacology and toxicology.

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.

Comparison of Canine and Human Physiological Factors: Understanding Interspecies Differences that Impact Drug Pharmacokinetics

This review is a summary of factors affecting the drug pharmacokinetics (PK) of dogs versus humans. Identifying these interspecies differences can facilitate canine-human PK extrapolations while providing mechanistic insights into species-specific drug in vivo behavior. Such a cross-cutting perspective can be particularly useful when developing therapeutics targeting diseases shared between the two species such as cancer, diabetes, cognitive dysfunction, and inflammatory bowel disease. Furthermore, recognizing these differences also supports a reverse PK extrapolations from humans to dogs. To appreciate the canine-human differences that can affect drug absorption, distribution, metabolism, and elimination, this review provides a comparison of the physiology, drug transporter/enzyme location, abundance, activity, and specificity between dogs and humans. Supplemental material provides an in-depth discussion of certain topics, offering additional critical points to consider. Based upon an assessment of available state-of-the-art information, data gaps were identified. The hope is that this manuscript will encourage the research needed to support an understanding of similarities and differences in human versus canine drug PK.

Prediction of Drug Clearance from Enzyme and Transporter Kinetics

Accurate estimation of in vivo clearance in human is pivotal to determine the dose and dosing regimen for drug development. In vitro-in vivo extrapolation (IVIVE) has been performed to predict drug clearance using empirical and physiological scalars. Multiple in vitro systems and mathematical modeling techniques have been employed to estimate in vivo clearance. The models for predicting clearance have significantly improved and have evolved to become more complex by integrating multiple processes such as drug metabolism and transport as well as passive diffusion. This chapter covers the use of conventional as well as recently developed methods to predict metabolic and transporter-mediated clearance along with the advantages and disadvantages of using these methods and the associated experimental considerations. The general approaches to improve IVIVE by use of appropriate scalars, incorporation of extrahepatic metabolism and transport and application of physiologically based pharmacokinetic (PBPK) models with proteomics data are also discussed. The chapter also provides an overview of the advantages of using such dynamic mechanistic models over static models for clearance predictions to improve IVIVE.

Examination of Physiologically-Based Pharmacokinetic Models of Rosuvastatin

Physiologically‐based pharmacokinetic (PBPK) modeling is increasingly used to predict drug disposition and drug–drug interactions (DDIs). However, accurately predicting the pharmacokinetics of transporter substrates and transporter‐mediated DDIs (tDDIs) is still challenging. Rosuvastatin is a commonly used substrate probe in DDI risk assessment for new molecular entities (NMEs) that are potential organic anion transporting polypeptide 1B or breast cancer resistance protein transporter inhibitors, and as such, several rosuvastatin PBPK models have been developed to try to predict the clinical DDI and support NME drug labeling. In this review, we examine five representative PBPK rosuvastatin models, discuss common challenges that the models have come across, and note remaining gaps. These shared learnings will help with the continuing efforts of rosuvastatin model validation, provide more information to understand transporter‐mediated drug disposition, and increase confidence in tDDI prediction.

A Novel Physiologically Based Model of Creatinine Renal Disposition to Integrate Current Knowledge of Systems Parameters and Clinical Observations

Creatinine is the most common clinical biomarker of renal function. As a substrate for renal transporters, its secretion is susceptible to inhibition by drugs, resulting in transient increase in serum creatinine and false impression of damage to kidney. Novel physiologically based models for creatinine were developed here and (dis)qualified in a stepwise manner until consistency with clinical data. Data from a matrix of studies were integrated, including systems data (common to all models), proteomics-informed in vitro-in vivo extrapolation of all relevant transporter clearances, exogenous administration of creatinine (to estimate endogenous synthesis rate), and inhibition of different renal transporters (11 perpetrator drugs considered for qualification during creatinine model development and verification on independent data sets). The proteomics-informed bottom-up approach resulted in the underprediction of creatinine renal secretion. Subsequently, creatinine-trimethoprim clinical data were used to inform key model parameters in a reverse translation manner, highlighting best practices and challenges for middle-out optimization of mechanistic models.

A Physiologically Based in Silico Tool to Assess the Risk of Drug-Related Crystalluria

Drug precipitation in the nephrons of the kidney can cause drug-induced crystal nephropathy (DICN). To aid mitigation of this risk in early drug discovery, we developed a physiologically based in silico model to predict DICN in rats, dogs, and humans. At a minimum, the likelihood of DICN is determined by the level of systemic exposure to the molecule, the molecule's physicochemical properties and the unique physiology of the kidney. Accordingly, the proposed model accounts for these properties in order to predict drug exposure relative to solubility along the nephron. Key physiological parameters of the kidney were codified in a manner consistent with previous reports. Quantitative structure-activity relationship models and in vitro assays were used to estimate drug-specific physicochemical inputs to the model. The proposed model was calibrated against urinary excretion data for 42 drugs, and the utility for DICN prediction is demonstrated through application to 20 additional drugs.

Using PBPK Modeling to Predict Drug Exposure and Support Dosage Adjustments in Patients With Renal Impairment: An Example with Lamivudine

BACKGROUND

Lamivudine is a nucleoside reverse transcriptase inhibitor used to treat HIV and hepatitis B. It is primarily cleared by the kidney with renal secretion mediated by OCT2 and MATE.

OBJECTIVE

To use PBPK modeling to assess the impact of renal impairment on lamivudine pharmacokinetics using the Simcyp® Simulator.

METHODS

The model incorporated the Simcyp® Mechanistic Kidney Model option to predict renal disposition. The model was initially verified using the Simcyp® Healthy Volunteer population. Two discrete patient populations were then created for moderate (GFR 10-40 mL/min) and severe (GFR < 10 mL/min) renal failure (RF), and model simulations were compared to published data. The developed model was then utilized in a clinical study evaluating the clinical experience and plasma exposure of lamivudine when administered at higher than recommended doses to HIV-infected patients with varying degrees of renal impairment.

RESULTS

Predicted systemic exposure metrics (Cmax, AUC) compared favorably to published clinical data for each population, with the following fold errors (FE, ratio of predicted and observed data) for Cmax/AUC: Healthy Volunteers 1.04/1.04, Moderate RF 1.03/0.78, Severe RF 0.89/0.79. The model captured lamivudine plasma concentrations measured pre- and post-dose (0.5-1.5hr) in study participants (n = 34). Model simulations demonstrated comparable systemic profiles across patient cohorts, supporting the proposed dosage adjustment scheme.

CONCLUSION

This study illustrates how PBPK modeling can help verify dosing guidelines for patients with varying levels of renal impairment. This approach may also be useful for predicting potential changes in exposure during renal insufficiency for compounds undergoing clinical development.

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.

Comparison of the urinary glucose excretion contributions of SGLT2 and SGLT1: A quantitative systems pharmacology analysis in healthy individuals and patients with type 2 diabetes treated with SGLT2 inhibitors

AIM

To develop a quantitative drug-disease systems model to investigate the paradox that sodium-glucose co-transporter (SGLT)2 is responsible for >80% of proximal tubule glucose reabsorption, yet SGLT2 inhibitor treatment results in only 30% to 50% less reabsorption in patients with type 2 diabetes mellitus (T2DM).

MATERIALS AND METHODS

A physiologically based four-compartment model of renal glucose filtration, reabsorption and excretion via SGLT1 and SGLT2 was developed as a system of ordinary differential equations using R/IQRtools. SGLT2 inhibitor pharmacokinetics and pharmacodynamics were estimated from published concentration-time profiles in plasma and urine and from urinary glucose excretion (UGE) in healthy people and people with T2DM.

RESULTS

The final model showed that higher renal glucose reabsorption in people with T2DM versus healthy people was associated with 54% and 28% greater transporter capacity for SGLT1 and SGLT2, respectively. Additionally, the analysis showed that UGE is highly dependent on mean plasma glucose and estimated glomerular filtration rate (eGFR) and that their consideration is critical for interpreting clinical UGE findings.

CONCLUSIONS

Quantitative drug-disease system modelling revealed mechanistic differences in renal glucose reabsorption and UGE between healthy people and those with T2DM, and clearly showed that SGLT2 inhibition significantly increased glucose available to SGLT1 downstream in the tubule. Importantly, we found that the findings of lower than expected UGE with SGLT2 inhibition are explained by the shift to SGLT1, which recovered additional glucose (~30% of total).

Quantitative Translation of Microfluidic Transporter in Vitro Data to in Vivo Reveals Impaired Albumin-Facilitated Indoxyl Sulfate Secretion in Chronic Kidney Disease

Indoxyl sulfate (IxS), a highly albumin-bound uremic solute, accumulates in chronic kidney disease (CKD) due to reduced renal clearance. This study was designed to specifically investigate the role of human serum albumin (HSA) in IxS renal secretion via organic anion transporter 1 (OAT1) in a microfluidic system and subsequently apply quantitative translation of in vitro data to predict extent of change in IxS renal clearance in CKD stage IV relative to healthy. Conditionally immortalized human proximal tubule epithelial cells overexpressing OAT1 were incubated with IxS (5-200 μM) in the HSA-free medium or in the presence of either HSA or CKD-modified HSA. IxS uptake in the presence of HSA resulted in more than 20-fold decrease in OAT1 affinity (K_m,u) and 37-fold greater in vitro unbound intrinsic clearance (CL_int,u) versus albumin-free condition. In the presence of CKD-modified albumin, K_m,u increased four-fold and IxS CL_int,u decreased almost seven-fold relative to HSA. Fold-change in parameters exceeded differences in IxS binding between albumin conditions, indicating additional mechanism and facilitating role of albumin in IxS OAT1-mediated uptake. Quantitative translation of IxS in vitro OAT1-mediated CL_int,u predicted a 60% decrease in IxS renal elimination as a result of CKD, in agreement with the observed data (80%). The findings of the current study emphasize the role of albumin in IxS transport via OAT1 and explored the impact of modifications in albumin on renal excretion via active secretion in CKD. For the first time, this study performed quantitative translation of transporter kinetic data generated in a novel microfluidic in vitro system to a clinically relevant setting. Knowledge gaps and future directions in quantitative translation of renal drug disposition from microphysiological systems are discussed.

A Comprehensive Framework for Physiologically-Based Pharmacokinetic Modeling in Matlab

Physiologically-based pharmacokinetic (PBPK) models are useful tools to predict clinical scenarios for special populations for whom there are high hurdles to conduct clinical trials such as children or the elderly. However, the coding of a PBPK model in a mathematical programming language can be challenging. This tutorial illustrates how to build a whole-body PBPK model in Matlab to answer specific pharmacological questions involving drug disposition and magnitudes of drug-drug interactions in different patient populations.

A critical review of adverse effects to the kidney: mechanisms, data sources, and in silico tools to assist prediction

INTRODUCTION

The kidney is a major target for toxicity elicited by pharmaceuticals and environmental pollutants. Standard testing which often does not investigate underlying mechanisms has proven not to be an adequate hazard assessment approach. As such, there is an opportunity for the application of computational approaches that utilize multiscale data based on the Adverse Outcome Pathway (AOP) paradigm, coupled with an understanding of the chemistry underpinning the molecular initiating event (MIE) to provide a deep understanding of how structural fragments of molecules relate to specific mechanisms of nephrotoxicity. Aims covered: The aim of this investigation was to review the current scientific landscape related to computational methods, including mechanistic data, AOPs, publicly available knowledge bases and current in silico models, for the assessment of pharmaceuticals and other chemicals with regard to their potential to elicit nephrotoxicity. A list of over 250 nephrotoxicants enriched with, where possible, mechanistic and AOP-derived understanding was compiled. Expert opinion: Whilst little mechanistic evidence has been translated into AOPs, this review identified a number of data sources of in vitro, in vivo, and human data that may assist in the development of in silico models which in turn may shed light on the interrelationships between nephrotoxicity mechanisms.

Physiologically Based Pharmacokinetic Modeling of Fimasartan, Amlodipine, and Hydrochlorothiazide for the Investigation of Drug-Drug Interaction Potentials

PURPOSE

To build a physiologically based pharmacokinetic (PBPK) model for fimasartan, amlodipine, and hydrochlorothiazide, and to investigate the drug-drug interaction (DDI) potentials.

METHODS

The PBPK model of each drug was developed using Simcyp software (Version 15.0), based on the information obtained from literature sources and in vitro studies. The predictive performance of the model was assessed by comparing the predicted PK profiles and parameters with the observed data collected from healthy subjects after multiple oral doses of fimasartan, amlodipine, and hydrochlorothiazide. The DDI potentials after co-administration of three drugs were simulated using the final model.

RESULTS

The predicted-to-observed ratios of all the pharmacokinetic parameters met the acceptance criterion. The PBPK model predicted no significant DDI when fimasartan was co-administered with amlodipine or hydrochlorothiazide, which is consistent with the observed clinical data. In the simulation of DDI at steady-state after co-administration of three drugs, the model predicted that fimasartan exposure would be increased by ~24.5%, while no changes were expected for the exposures of amlodipine and hydrochlorothiazide.

CONCLUSIONS

The developed PBPK model adequately predicted the pharmacokinetics of fimasartan, amlodipine, and hydrochlorothiazide, suggesting that the model can be used to further investigate the DDI potential of each drug.

Development of a Dynamic Physiologically Based Mechanistic Kidney Model to Predict Renal Clearance

Renal clearance is usually predicted via empirical approaches including quantitative structure activity relationship and allometric scaling. Recently, mechanistic prediction approaches using in silico kidney models have been proposed. However, empirical scaling factors are typically used to adjust for either passive diffusion or active secretion, to acceptably predict renal clearances. The goal of this study was to establish a renal clearance simulation tool that allows prediction of renal clearance (filtration and pH-dependent passive reabsorption) from in vitro permeability data. A 35-compartment physiologically based mechanistic kidney model was developed based on human physiology. The model was verified using 46 test compounds, including neutrals, acids, bases, and zwitterions. The feasibility of incorporating active secretion and pH-dependent bidirectional passive diffusion into the model was demonstrated using para-aminohippuric acid (PAH), cimetidine, memantine, and salicylic acid. The developed model enables simulation of renal clearance from in vitro permeability data, with predicted renal clearance within twofold of observed for 87% of the test drugs.

Physiologically Based Pharmacokinetic Modelling and Prediction of Metformin Pharmacokinetics in Renal/Hepatic-Impaired Young Adults and Elderly Populations

BACKGROUND AND OBJECTIVES

Physiologically based pharmacokinetic (PBPK) modelling and simulation enable researchers to overcome practical limitations for clinical trials on special populations. This study was conducted to investigate how the PBPK model describes the pharmacokinetics of metformin in young adult and elderly populations and to predict the pharmacokinetics of metformin in patients with renal or hepatic impairment in both populations.

METHODS

A first-order absorption/PBPK model for metformin was built in the Simcyp simulator version 14 release 1. A full PBPK model was constructed for metformin based on physicochemical properties and clinical observations. The model was refined and validated using clinical plasma concentration data obtained in healthy young adults and elderly after the oral administration of metformin. Metformin pharmacokinetics in patients with renal or hepatic impairment were then investigated and compared by simulation.

RESULTS

The PBPK model reasonably predicted the pharmacokinetic profiles of metformin for both young adults and the elderly. The predicted pharmacokinetic parameters, including maximum concentration, area under the time-concentration curve, and apparent oral clearance values, were within 1.5-fold of the observed data of metformin. In the simulation results, the systemic exposure of metformin was expected to be markedly increased not only with a decrease in renal function but also with severe hepatic impairments.

CONCLUSIONS

The PBPK model adequately characterised the pharmacokinetics of metformin in both young adult and elderly populations. PBPK modelling and simulation can be used as a useful tool to investigate and compare the pharmacokinetics in geriatric populations incorporating various disease conditions.

Evaluation and Quantitative Prediction of Renal Transporter-Mediated Drug-Drug Interactions

With numerous drugs cleared renally, inhibition of uptake transporters localized on the basolateral membrane of renal proximal tubule cells, eg, organic anion transporters (OATs) and organic cation transporters (OCTs), may lead to clinically meaningful drug-drug interactions (DDIs). Additionally, clinical evidence for the possible involvement of efflux transporters, such as P-glycoprotein (P-gp) and multidrug and toxin extrusion protein 1/2-K (MATE1/2-K), in the renal DDIs is emerging. Herein, we review recent progress regarding mechanistic understanding of transporter-mediated renal DDIs as well as the quantitative predictability of renal DDIs using static and physiologically based pharmacokinetic (PBPK) models. Generally, clinical DDI data suggest that the magnitude of plasma exposure changes attributable to renal DDIs is less than 2-fold, unlike the DDIs associated with inhibition of cytochrome P-450s and/or hepatic uptake transporters. It is concluded that although there is a need for risk assessment early in drug development, current available data imply that safety concerns related to the renal DDIs are generally low. Nevertheless, consideration must be given to the therapeutic index of the victim drug and potential risk in a specific patient population (eg, renal impairment). Finally, in vitro transporter data and clinical pharmacokinetic parameters obtained from the first-in-human studies have proven useful in support of quantitative prediction of DDIs associated with inhibition of renal secretory transporters, OATs or OCTs.

The Application of Physiologically Based Pharmacokinetic Modeling to Predict the Role of Drug Transporters: Scientific and Regulatory Perspectives

Transporters play an important role in drug absorption, disposition, and drug action. The evaluation of drug transporters requires a comprehensive understanding of transporter biology and pharmacology. Physiologically based pharmacokinetic (PBPK) models may offer an integrative platform to quantitatively evaluate the role of drug transporters and its interplay with other drug disposition processes such as passive drug diffusion and elimination by metabolizing enzymes. To date, PBPK modeling and simulations integrating drug transporters lag behind that for drug-metabolizing enzymes. In addition, predictive performance of PBPK has not been well established for predicting the role of drug transporters in the pharmacokinetics of a drug. To enhance overall predictive performance of transporter-based PBPK models, it is necessary to have a detailed understanding of transporter biology for proper representation in the models and to have a quantitative understanding of the contribution of transporters in the absorption and metabolism of a drug. This article summarizes PBPK-based submissions evaluating the role of drug transporters to the Office of Clinical Pharmacology of the US Food and Drug Administration.

More Power to OATP1B1: An Evaluation of Sample Size in Pharmacogenetic Studies Using a Rosuvastatin PBPK Model for Intestinal, Hepatic, and Renal Transporter-Mediated Clearances

Rosuvastatin is a substrate of choice in clinical studies of organic anion-transporting polypeptide (OATP)1B1- and OATP1B3-associated drug interactions; thus, understanding the effect of OATP1B1 polymorphisms on the pharmacokinetics of rosuvastatin is crucial. Here, physiologically based pharmacokinetic (PBPK) modeling was coupled with a power calculation algorithm to evaluate the influence of sample size on the ability to detect an effect (80% power) of OATP1B1 phenotype on pharmacokinetics of rosuvastatin. Intestinal, hepatic, and renal transporters were mechanistically incorporated into a rosuvastatin PBPK model using permeability-limited models for intestine, liver, and kidney, respectively, nested within a full PBPK model. Simulated plasma rosuvastatin concentrations in healthy volunteers were in agreement with previously reported clinical data. Power calculations were used to determine the influence of sample size on study power while accounting for OATP1B1 haplotype frequency and abundance in addition to its correlation with OATP1B3 abundance. It was determined that 10 poor-transporter and 45 intermediate-transporter individuals are required to achieve 80% power to discriminate the AUC0-48h of rosuvastatin from that of the extensive-transporter phenotype. This number was reduced to 7 poor-transporter and 40 intermediate-transporter individuals when the reported correlation between OATP1B1 and 1B3 abundance was taken into account. The current study represents the first example in which PBPK modeling in conjunction with power analysis has been used to investigate sample size in clinical studies of OATP1B1 polymorphisms. This approach highlights the influence of interindividual variability and correlation of transporter abundance on study power and should allow more informed decision making in pharmacogenomic study design.

Prediction of Transporter-Mediated Drug-Drug Interactions for Baricitinib

Baricitinib, an oral selective Janus kinase 1 and 2 inhibitor, undergoes active renal tubular secretion. Baricitinib was not predicted to inhibit hepatic and renal uptake and efflux drug transporters, based on the ratio of the unbound maximum eliminating-organ inlet concentration and the in vitro half-maximal inhibitory concentrations (IC₅₀ ). In vitro, baricitinib was a substrate for organic anion transporter (OAT)3, multidrug and toxin extrusion protein (MATE)2-K, P-glycoprotein (P-gp), and breast cancer resistance protein (BCRP). Probenecid, a strong OAT3 inhibitor, increased the area under the concentration-time curve from time zero to infinity (AUC_[0-∞] ) of baricitinib by twofold and decreased renal clearance to 69% of control in healthy subjects. Physiologically based pharmacokinetic (PBPK) modeling reproduced the renal clearance of baricitinib and the inhibitory effect of probenecid using the in vitro IC₅₀ value of 4.4 μM. Using ibuprofen and diclofenac in vitro IC₅₀ values of 4.4 and 3.8 μM toward OAT3, 1.2 and 1.0 AUC_(0-∞) ratios of baricitinib were predicted. These predictions suggest clinically relevant drug-drug interactions (DDIs) with ibuprofen and diclofenac are unlikely.

Systems Toxicology: Real World Applications and Opportunities

Systems Toxicology aims to change the basis of how adverse biological effects of xenobiotics are characterized from empirical end points to describing modes of action as adverse outcome pathways and perturbed networks. Toward this aim, Systems Toxicology entails the integration of in vitro and in vivo toxicity data with computational modeling. This evolving approach depends critically on data reliability and relevance, which in turn depends on the quality of experimental models and bioanalysis techniques used to generate toxicological data. Systems Toxicology involves the use of large-scale data streams ("big data"), such as those derived from omics measurements that require computational means for obtaining informative results. Thus, integrative analysis of multiple molecular measurements, particularly acquired by omics strategies, is a key approach in Systems Toxicology. In recent years, there have been significant advances centered on in vitro test systems and bioanalytical strategies, yet a frontier challenge concerns linking observed network perturbations to phenotypes, which will require understanding pathways and networks that give rise to adverse responses. This summary perspective from a 2016 Systems Toxicology meeting, an international conference held in the Alps of Switzerland, describes the limitations and opportunities of selected emerging applications in this rapidly advancing field. Systems Toxicology aims to change the basis of how adverse biological effects of xenobiotics are characterized, from empirical end points to pathways of toxicity. This requires the integration of in vitro and in vivo data with computational modeling. Test systems and bioanalytical technologies have made significant advances, but ensuring data reliability and relevance is an ongoing concern. The major challenge facing the new pathway approach is determining how to link observed network perturbations to phenotypic toxicity.

Delineating the Role of Various Factors in Renal Disposition of Digoxin through Application of Physiologically Based Kidney Model to Renal Impairment Populations

Development of submodels of organs within physiologically-based pharmacokinetic (PBPK) principles and beyond simple perfusion limitations may be challenging because of underdeveloped in vitro-in vivo extrapolation approaches or lack of suitable clinical data for model refinement. However, advantage of such models in predicting clinical observations in divergent patient groups is now commonly acknowledged. Mechanistic understanding of altered renal secretion in renal impairment is one area that may benefit from such models, despite knowledge gaps in renal pathophysiology. In the current study, a PBPK kidney model was developed for digoxin, accounting for the roles of organic anion transporting peptide 4C1 (OATP4C1) and P-glycoprotein (P-gp) in its tubular secretion, with the aim to investigate the impact of age and renal impairment (moderate to severe) on renal drug disposition. Initial PBPK simulations based on changes in glomerular filtration rate (GFR) underestimated the observed reduction in digoxin renal excretion clearance (CLR) in subjects with moderately impaired renal function relative to healthy. Reduction in either proximal tubule cell number or the OATP4C1 abundance in the mechanistic kidney model successfully predicted 59% decrease in digoxin CLR, in particular when these changes were proportional to reduction in GFR. In contrast, predicted proximal tubule concentration of digoxin was only sensitive to changes in the transporter expression/ million proximal tubule cells. Based on the mechanistic modeling, reduced proximal tubule cellularity and OATP4C1 abundance, and inhibition of OATP4C1-mediated transport, are proposed as possible causes of reduced digoxin renal secretion in renally impaired patients.

Key to Opening Kidney for In Vitro-In Vivo Extrapolation Entrance in Health and Disease: Part II: Mechanistic Models and In Vitro-In Vivo Extrapolation

It is envisaged that application of mechanistic models will improve prediction of changes in renal disposition due to drug-drug interactions, genetic polymorphism in enzymes and transporters and/or renal impairment. However, developing and validating mechanistic kidney models is challenging due to the number of processes that may occur (filtration, secretion, reabsorption and metabolism) in this complex organ. Prediction of human renal drug disposition from preclinical species may be hampered by species differences in the expression and activity of drug metabolising enzymes and transporters. A proposed solution is bottom-up prediction of pharmacokinetic parameters based on in vitro-in vivo extrapolation (IVIVE), mediated by recent advances in in vitro experimental techniques and application of relevant scaling factors. This review is a follow-up to the Part I of the report from the 2015 AAPS Annual Meeting and Exhibition (Orlando, FL; 25th-29th October 2015) which focuses on IVIVE and mechanistic prediction of renal drug disposition. It describes the various mechanistic kidney models that may be used to investigate renal drug disposition. Particular attention is given to efforts that have attempted to incorporate elements of IVIVE. In addition, the use of mechanistic models in prediction of renal drug-drug interactions and potential for application in determining suitable adjustment of dose in kidney disease are discussed. The need for suitable clinical pharmacokinetics data for the purposes of delineating mechanistic aspects of kidney models in various scenarios is highlighted.

Key to Opening Kidney for In Vitro-In Vivo Extrapolation Entrance in Health and Disease: Part I: In Vitro Systems and Physiological Data

The programme for the 2015 AAPS Annual Meeting and Exhibition (Orlando, FL; 25-29 October 2015) included a sunrise session presenting an overview of the state-of-the-art tools for in vitro-in vivo extrapolation (IVIVE) and mechanistic prediction of renal drug disposition. These concepts are based on approaches developed for prediction of hepatic clearance, with consideration of scaling factors physiologically relevant to kidney and the unique and complex structural organisation of this organ. Physiologically relevant kidney models require a number of parameters for mechanistic description of processes, supported by quantitative information on renal physiology (system parameters) and in vitro/in silico drug-related data. This review expands upon the themes raised during the session and highlights the importance of high quality in vitro drug data generated in appropriate experimental setup and robust system-related information for successful IVIVE of renal drug disposition. The different in vitro systems available for studying renal drug metabolism and transport are summarised and recent developments involving state-of-the-art technologies highlighted. Current gaps and uncertainties associated with system parameters related to human kidney for the development of physiologically based pharmacokinetic (PBPK) model and quantitative prediction of renal drug disposition, excretion, and/or metabolism are identified.

Development of a physiologically based pharmacokinetic model of actinomycin D in children with cancer

Aims

Use of the anti‐tumour antibiotic actinomycin D is associated with development of hepatotoxicity, particularly in young children. A paucity of actinomycin D pharmacokinetic data make it challenging to develop a sound rationale for defining dosing regimens in younger patients. The study aim was to develop a physiologically based pharmacokinetic (PBPK) model using a combination of data from the literature and generated from experimental analyses.

Methods

Assays to determine actinomycin D Log P, blood:plasma partition ratio and ABCB1 kinetics were conducted. These data were combined with physiochemical properties sourced from the literature to generate a compound file for use within the modelling‐simulation software Simcyp (version 14 release 1). For simulation, information was taken from two datasets, one from 117 patients under the age of 21 and one from 20 patients aged 16–48.

Results

The final model incorporated clinical renal and biliary clearance data and an additional systemic clearance value. The mean AUC_0‐26h of simulated subjects was within 1.25‐fold of the observed AUC_0‐26h (84 ng h ml⁻¹ simulated vs. 93 ng h ml⁻¹ observed). For the younger age ranges, AUC predictions were within two‐fold of observed values, with simulated data from six of the eight age/dose ranges falling within 15% of observed data. Simulated values for actinomycin D AUC_0‐26h and clearance in infants aged 0–12 months ranged from 104 to 115 ng h ml⁻¹ and 3.5–3.8 l h⁻¹, respectively.

Conclusions

The model has potential utility for prediction of actinomycin D exposure in younger patients and may help guide future dosing. However, additional independent data from neonates and infants is needed for further validation. Physiological differences between paediatric cancer patients and healthy children also need to be further characterized and incorporated into PBPK models.

Development of a Multicompartment Permeability-Limited Lung PBPK Model and Its Application in Predicting Pulmonary Pharmacokinetics of Antituberculosis Drugs

Achieving sufficient concentrations of antituberculosis (TB) drugs in pulmonary tissue at the optimum time is still a challenge in developing therapeutic regimens for TB. A physiologically based pharmacokinetic model incorporating a multicompartment permeability-limited lung model was developed and used to simulate plasma and pulmonary concentrations of seven drugs. Passive permeability of drugs within the lung was predicted using an in vitro-in vivo extrapolation approach. Simulated epithelial lining fluid (ELF):plasma concentration ratios showed reasonable agreement with observed clinical data for rifampicin, isoniazid, ethambutol, and erythromycin. For clarithromycin, itraconazole and pyrazinamide the observed ELF:plasma ratios were significantly underpredicted. Sensitivity analyses showed that changing ELF pH or introducing efflux transporter activity between lung tissue and ELF can alter the ELF:plasma concentration ratios. The described model has shown utility in predicting the lung pharmacokinetics of anti-TB drugs and provides a framework for predicting pulmonary concentrations of novel anti-TB drugs.

Towards quantitation of the effects of renal impairment and probenecid inhibition on kidney uptake and efflux transporters, using physiologically based pharmacokinetic modelling and simulations

Background and Objectives

The kidney is a major drug-eliminating organ. Renal impairment or concomitant use of transporter inhibitors may decrease active secretion and increase exposure to a drug that is a substrate of kidney secretory transporters. However, prediction of the effects of patient factors on kidney transporters remains challenging because of the multiplicity of transporters and the lack of understanding of their abundance and specificity. The objective of this study was to use physiologically based pharmacokinetic (PBPK) modelling to evaluate the effects of patient factors on kidney transporters.

Methods

Models for three renally cleared drugs (oseltamivir carboxylate, cidofovir and cefuroxime) were developed using a general PBPK platform, with the contributions of net basolateral uptake transport (T_up,b) and apical efflux transport (T_eff,a) being specifically defined.

Results and Conclusion

We demonstrated the practical use of PBPK models to: (1) define transporter-mediated renal secretion, using plasma and urine data; (2) inform a change in the system-dependent parameter (≥10-fold reduction in the functional ‘proximal tubule cells per gram kidney’) in severe renal impairment that is responsible for the decreased secretory transport activities of test drugs; (3) derive an in vivo, plasma unbound inhibition constant of T_up,b by probenecid (≤1 μM), based on observed drug interaction data; and (4) suggest a plausible mechanism of probenecid preferentially inhibiting T_up,b in order to alleviate cidofovir-induced nephrotoxicity.

Electronic supplementary material

The online version of this article (doi:10.1007/s40262-013-0117-y) contains supplementary material, which is available to authorized users.

Application of permeability-limited physiologically-based pharmacokinetic models: part I-digoxin pharmacokinetics incorporating P-glycoprotein-mediated efflux

A prerequisite for the prediction of the magnitude of P-glycoprotein (P-gp)-mediated drug-drug interactions between digoxin and P-gp inhibitors (e.g. verapamil and its metabolite norverapamil) or P-gp inducers (e.g. rifampicin) is a predictive pharmacokinetic model for digoxin itself. Thus, relevant in vitro metabolic, transporter and inhibitory data incorporated into permeability-limited models, such as the "advanced dissolution, absorption and metabolism" (ADAM) module and the permeability-limited liver (PerL) module, integrated with a mechanistic physiologically-based pharmacokinetic (PBPK) model such as that of the Simcyp Simulator (version 12.2) are necessary. Simulated concentration-time profiles of digoxin generated using the developed model were consistent with observed data across 31 independent studies [13 intravenous single dose (SD), 12 per oral SD and six multiple dose studies]. The fact that predicted tmax (time of maximum plasma concentration observed) and Cmax (maximum plasma concentration observed) of oral digoxin were similar to observed values indicated that the relative contributions of permeation and P-gp-mediated efflux in the model were appropriate. There was no indication of departure from dose proportionality over the dose range studied (0.25-1.5 mg). All dose normalised area under the plasma concentration-time curve profiles (AUCs) for the 0.25, 0.5, 0.75 and 1 mg doses resembled each other. Thus, PBPK modelling in conjunction with mechanistic absorption and distribution models and reliable in vitro transporter data can be used to assess the impact of dose on P-gp-mediated efflux (or otherwise).

Application of permeability-limited physiologically-based pharmacokinetic models: part II - prediction of P-glycoprotein mediated drug-drug interactions with digoxin

Digoxin is the recommended substrate for assessment of P-glycoprotein (P-gp)-mediated drug-drug interactions (DDIs) in vivo. The overall aim of our study was to investigate the inhibitory potential of both verapamil and norverapamil on the P-gp-mediated efflux of digoxin in both gut and liver. Therefore, a physiologically-based pharmacokinetic (PBPK) model for verapamil and its primary metabolite was developed and validated through the recovery of observed clinical plasma concentration data for both moieties and the reported interaction with midazolam, albeit a cytochrome P450 3A4-mediated DDI. The validated inhibitor model was then used in conjunction with the model developed previously for digoxin. The range of values obtained for the 10 trials indicated that increases in area under the plasma concentration-time curve (AUC) profiles and maximum plasma concentration observed (Cmax ) values of digoxin following administration of verapamil were more comparable with in vivo observations, when P-gp inhibition by the metabolite, norverapamil, was considered as well. The predicted decrease in AUC and Cmax values of digoxin following administration of rifampicin because of P-gp induction was 1.57- (range: 1.42-1.77) and 1.62-fold (range: 1.53-1.70), which were reasonably consistent with observed values of 1.4- and 2.2-fold, respectively. This study demonstrates the application of permeability-limited models of absorption and distribution within a PBPK framework together with relevant in vitro data on transporters to assess the clinical impact of modulated P-gp-mediated efflux by drugs in development.