Albumin-Mediated Drug Uptake by Organic Anion Transporter 1/3 Is Real: Implications for the Prediction of Active Renal Secretion Clearance

Modulation of the transport-mediated active uptake by human serum albumin (HSA) for highly protein-bound substrates has been reported and improved the in vitro-to-in vivo extrapolation (IVIVE) of hepatic clearance. However, evidence for the relevance of such a phenomenon in the case of renal transporters is sparse. In this study, transport of renal organic anion transporter 1 or 3 (OAT1/3) substrates into conditionally immortalized proximal tubular epithelial cells transduced with OAT1/3 was measured in the presence and absence of 1 and 4% HSA while keeping the unbound substrate concentration constant (based on measured fraction unbound, fu,inc). In the presence of 4% HSA, the unbound intrinsic active uptake clearance (CLint,u,active) of six highly protein-bound substrates increased substantially relative to the HSA-free control (3.5- to 122-fold for the OAT1 CLint,u,active, and up to 28-fold for the OAT3 CLint,u,active). The albumin-mediated uptake effect (fold increase in CLint,u,active) was more pronounced with highly bound substrates compared to no effect seen for weakly protein-bound substrates adefovir (OAT1-specific) and oseltamivir carboxylate (OAT3-specific). The relationship between OAT1/3 CLint,u,active and fu,inc agreed with the facilitated-dissociation model; a relationship was established between the albumin-mediated fold change in CLint,u,active and fu,inc for both the OAT1 and OAT3, with implications for IVIVE modeling. The relative activity factor and the relative expression factor based on global proteomic quantification of in vitro OAT1/3 expression were applied for IVIVE of renal clearance. The inclusion of HSA improved the bottom-up prediction of the level of OAT1/3-mediated secretion and renal clearance (CLsec and CLr), in contrast to the underprediction observed with the control (HSA-free) scenario. For the first time, this study confirmed the presence of the albumin-mediated uptake effect with renal OAT1/3 transporters; the extent of the effect was more pronounced for highly protein-bound substrates. We recommend the inclusion of HSA in routine in vitro OAT1/3 assays due to considerable improvements in the IVIVE of CLsec and CLr.

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

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

Recent Progress on Physiologically Based Pharmacokinetic (PBPK) Model: A Review Based on Bibliometrics

Physiologically based pharmacokinetic/toxicokinetic (PBPK/PBTK) models are designed to elucidate the mechanism of chemical compound action in organisms based on the physiological, biochemical, anatomical, and thermodynamic properties of organisms. After nearly a century of research and practice, good results have been achieved in the fields of medicine, environmental science, and ecology. However, there is currently a lack of a more systematic review of progress in the main research directions of PBPK models, especially a more comprehensive understanding of the application in aquatic environmental research. In this review, a total of 3974 articles related to PBPK models from 1996 to 24 March 2024 were collected. Then, the main research areas of the PBPK model were categorized based on the keyword co-occurrence maps and cluster maps obtained by CiteSpace. The results showed that research related to medicine is the main application area of PBPK. Four major research directions included in the medical field were "drug assessment", "cross-species prediction", "drug-drug interactions", and "pediatrics and pregnancy drug development", in which "drug assessment" accounted for 55% of the total publication volume. In addition, bibliometric analyses indicated a rapid growth trend in the application in the field of environmental research, especially in predicting the residual levels in organisms and revealing the relationship between internal and external exposure. Despite facing the limitation of insufficient species-specific parameters, the PBPK model is still an effective tool for improving the understanding of chemical-biological effectiveness and will provide a theoretical basis for accurately assessing potential risks to ecosystems and human health. The combination with the quantitative structure-activity relationship model, Bayesian method, and machine learning technology are potential solutions to the previous research gaps.

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.

Determining the Effects of Chronic Kidney Disease on Organic Anion Transporter1/3 Activity Through Physiologically Based Pharmacokinetic Modeling

BACKGROUND AND OBJECTIVE

The renal excretion of drugs via organic anion transporters 1 and 3 (OAT1/3) is significantly decreased in patients with renal impairment. This study uses physiologically based pharmacokinetic models to quantify the reduction in OAT1/3-mediated secretion of drugs throughout varying stages of chronic kidney disease.

METHODS

Physiologically based pharmacokinetic models were constructed for four OAT1/3 substrates in healthy individuals: acyclovir, meropenem, furosemide, and ciprofloxacin. Observed data from drug-drug interaction studies with probenecid, a potent OAT1/3 inhibitor, were used to parameterize the contribution of OAT1/3 to the renal elimination of each drug. The models were then translated to patients with chronic kidney disease by accounting for changes in glomerular filtration rate, kidney volume, renal blood flow, plasma protein binding, and hematocrit. Additionally, a relationship was derived between the estimated glomerular filtration rate and the reduction in OAT1/3-mediated secretion of drugs based on the renal extraction ratios of ƿ-aminohippuric acid in patients with varying degrees of renal impairment. The relationship was evaluated in silico by evaluating the predictive performance of each final model in describing the pharmacokinetics (PK) of drugs across stages of chronic kidney disease.

RESULTS

OAT1/3-mediated renal excretion of drugs was found to be decreased by 27-49%, 50-68%, and 70-96% in stage 3, stage 4, and stage 5 of chronic kidney disease, respectively. In support of the parameterization, physiologically based pharmacokinetic models of four OAT1/3 substrates were able to adequately characterize the PK in patients with different degrees of renal impairment. Total exposure after intravenous administration was predicted within a 1.5-fold error and 85% of the observed data points fell within a 1.5-fold prediction error. The models modestly under-predicted plasma concentrations in patients with end-stage renal disease undergoing intermittent hemodialysis. However, results should be interpreted with caution because of the limited number of molecules analyzed and the sparse sampling in observed chronic kidney disease pharmacokinetic studies.

CONCLUSIONS

A quantitative understanding of the reduction in OAT1/3-mediated excretion of drugs in differing stages of renal impairment will contribute to better predictive accuracy for physiologically based pharmacokinetic models in drug development, assisting with clinical trial planning and potentially sparing this population from unnecessary toxic exposures.

A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Drug-Drug-Gene Interaction Model of Metformin and Cimetidine in Healthy Adults and Renally Impaired Individuals

Background

Metformin is a widely prescribed antidiabetic BCS Class III drug (low permeability) that depends on active transport for its absorption and disposition. It is recommended by the US Food and Drug Administration as a clinical substrate of organic cation transporter 2/multidrug and toxin extrusion protein for drug–drug interaction studies. Cimetidine is a potent organic cation transporter 2/multidrug and toxin extrusion protein inhibitor.

Objective

The objective of this study was to provide mechanistic whole-body physiologically based pharmacokinetic models of metformin and cimetidine, built and evaluated to describe the metformin-SLC22A2 808G>T drug–gene interaction, the cimetidine-metformin drug–drug interaction, and the impact of renal impairment on metformin exposure.

Methods

Physiologically based pharmacokinetic models were developed in PK-Sim^® (version 8.0). Thirty-nine clinical studies (dosing range 0.001–2550 mg), providing metformin plasma and urine data, positron emission tomography measurements of tissue concentrations, studies in organic cation transporter 2 polymorphic volunteers, drug–drug interaction studies with cimetidine, and data from patients in different stages of chronic kidney disease, were used to develop the metformin model. Twenty-seven clinical studies (dosing range 100–800 mg), reporting cimetidine plasma and urine concentrations, were used for the cimetidine model development.

Results

The established physiologically based pharmacokinetic models adequately describe the available clinical data, including the investigated drug–gene interaction, drug–drug interaction, and drug–drug–gene interaction studies, as well as the metformin exposure during renal impairment. All modeled drug–drug interaction area under the curve and maximum concentration ratios are within 1.5-fold of the observed ratios. The clinical data of renally impaired patients shows the expected increase in metformin exposure with declining kidney function, but also indicates counter-regulatory mechanisms in severe renal disease; these mechanisms were implemented into the model based on findings in preclinical species.

Conclusions

Whole-body physiologically based pharmacokinetic models of metformin and cimetidine were built and qualified for the prediction of metformin pharmacokinetics during drug–gene interaction, drug–drug interaction, and different stages of renal disease. The model files will be freely available in the Open Systems Pharmacology model repository. Current guidelines for metformin treatment of renally impaired patients should be reviewed to avoid overdosing in CKD3 and to allow metformin therapy of CKD4 patients.

Electronic supplementary material

The online version of this article (10.1007/s40262-020-00896-w) contains supplementary material, which is available to authorized users.

Application of PBPK Modeling and Simulation for Regulatory Decision Making and Its Impact on US Prescribing Information: An Update on the 2018-2019 Submissions to the US FDA's Office of Clinical Pharmacology

Since 2016, results from physiologically based pharmacokinetic (PBPK) analyses have been routinely found in the clinical pharmacology section of regulatory applications submitted to the US Food and Drug Administration (FDA). In 2018, the Food and Drug Administration's Office of Clinical Pharmacology published a commentary summarizing the application of PBPK modeling in the submissions it received between 2008 and 2017 and its impact on prescribing information. In this commentary, we provide an update on the application of PBPK modeling in submissions received between 2018 and 2019 and highlight a few notable examples.

Diclofenac potentiates the antitumor effect of cisplatin in a xenograft mouse model transplanted with cisplatin-resistant cells without enhancing cisplatin-induced nephrotoxicity

Cisplatin (CDDP) is a well-known anticancer agent, and CDDP-induced nephrotoxicity (CIN) is one of the most serious adverse effects. Previously, we revealed that while celecoxib reduces CIN, diclofenac does not appear to enhance it. Furthermore, we reported that diclofenac additively enhances the cytotoxic effect of CDDP on CDDP-resistant A549 cells (A549/DDP cells) and their spheroids. In addition, celecoxib reduces the cytotoxic effect of CDDP on A549/DDP cells while demonstrating an anticancer effect; however, it enhanced the effect of CDDP cytotoxicity on spheroids. Therefore, we evaluated the effects of diclofenac or celecoxib on CIN and the antitumor effect of CDDP in a xenograft mouse model transplanted with A549/DDP cells. Although CDDP did not decrease tumor size and tumor weight, these parameters were significantly reduced following co-administration with diclofenac when compared with the control group. Conversely, celecoxib marginally suppressed the antitumor effect of CDDP. Moreover, CDDP increased the mRNA levels of kidney injury molecule 1 (Kim-1), a renal disorder marker, in the kidneys of xenograft mice; treatment with celecoxib and diclofenac did not impact Kim-1 mRNA levels increased by CDDP. In conclusion, diclofenac potentiated the antitumor effect of CDDP without enhancing CIN.

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.

Identification of the key target profiles underlying the drugs of narrow therapeutic index for treating cancer and cardiovascular disease

An appropriate therapeutic index is crucial for drug discovery and development since narrow therapeutic index (NTI) drugs with slight dosage variation may induce severe adverse drug reactions or potential treatment failure. To date, the shared characteristics underlying the targets of NTI drugs have been explored by several studies, which have been applied to identify potential drug targets. However, the association between the drug therapeutic index and the related disease has not been dissected, which is important for revealing the NTI drug mechanism and optimizing drug design. Therefore, in this study, two classes of disease (cancers and cardiovascular disorders) with the largest number of NTI drugs were selected, and the target property of the corresponding NTI drugs was analyzed. By calculating the biological system profiles and human protein–protein interaction (PPI) network properties of drug targets and adopting an AI-based algorithm, differentiated features between two diseases were discovered to reveal the distinct underlying mechanisms of NTI drugs in different diseases. Consequently, ten shared features and four unique features were identified for both diseases to distinguish NTI from NNTI drug targets. These computational discoveries, as well as the newly found features, suggest that in the clinical study of avoiding narrow therapeutic index in those diseases, the ability of target to be a hub and the efficiency of target signaling in the human PPI network should be considered, and it could thus provide novel guidance in the drug discovery and clinical research process and help to estimate the drug safety of cancer and cardiovascular disease.

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.

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.

Utilization of Physiologically Based Pharmacokinetic Modeling in Clinical Pharmacology and Therapeutics: an Overview

The purpose of this review was to assess the advancement of applications for physiologically based pharmacokinetic (PBPK) modeling in various therapeutic areas. We conducted a PubMed search, and 166 articles published between 2012 and 2018 on FDA-approved drug products were selected for further review. Qualifying publications were summarized according to therapeutic area, medication(s) studied, pharmacokinetic model type utilized, simulator program used, and the applications of that modeling. The results showed a 13-fold increase in the number of papers published from 2012 to 2018, with the largest proportion of articles dedicated to the areas of infectious diseases, oncology, and neurology, and application extensions including prediction of drug-drug interactions due to metabolism and/or transporter-mediated effects and understanding drug kinetics in special populations. In addition, we profiled several high-impact studies whose results were used to guide package insert information and formulate dose recommendations. These results show that while utilization of PBPK modeling has drastically increased over the past several years, regulatory support, lack of easy-to-use systems for clinicians, and challenges with model validation remain major challenges for the widespread adoption of this practice in institutional and ambulatory settings. However, PBPK modeling will continue to be a useful tool in the future to assess therapeutic drug monitoring and the growing field of personalized medicine.

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.

Organic anion transporters and PI3K-AKT-mTOR pathway mediate the synergistic anticancer effect of pemetrexed and rhein

The aim of this study was to explore whether rhein could enhance the effects of pemetrexed (PTX) on the therapy of non-small-cell lung cancer (NSCLC) and to clarify the associated molecular mechanism. Our study shows that rhein in combination with PTX could obviously increase the systemic exposure of PTX in rats, which would be mediated by the inhibition of organic anion transporters (OATs). Furthermore, the toxicity of PTX was significantly raised by rhein in A549 cells in a concentration-dependent manner. Concomitant administration of rhein and PTX-induced cell apoptosis compared with PTX alone in flow cytometry assays, which was further validated by the protein expressions of the apoptotic markers B-cell lymphoma-2/Bcl-2-associated x (Bcl-2/Bax) and Cleaved-Caspase3 (Cl-Caspase3). Meanwhile, the results of monodansylcadaverine (MDC) dyeing experiments showed that PTX-induced autophagy could be enhanced by combination therapy with rhein in A549 cells. Western blot analysis indicated that the synergistic effect of rhein on PTX-mediated autophagy may be interrelated to PI3K-AKT-mTOR pathway inhibition and to the enhancement of p-AMPK and light chain 3-II (LC3-II) protein levels. From these findings, it could be surmised that rhein enhanced the antitumor activity of PTX through influencing autophagy and apoptosis by modulating the PI3K-AKT-mTOR pathway and Bcl-2 family of proteins in A549 cells. Our findings demonstrated that the potential application of rhein as a candidate drug in combination with PTX is promising for treatment of the human lung cancer.

Multiple circulating saponins from intravenous ShenMai inhibit OATP1Bs in vitro: potential joint precipitants of drug interactions

ShenMai, an intravenous injection prepared from steamed Panax ginseng roots (Hongshen) and Ophiopogon japonicus roots (Maidong), is used as an add-on therapy for coronary artery disease and cancer; saponins are its bioactive constituents. Since many saponins inhibit human organic anion-transporting polypeptides (OATP)1B, this investigation determined the inhibition potencies of circulating ShenMai saponins on the transporters and the joint potential of these compounds for ShenMai-drug interaction. Circulating saponins and their pharmacokinetics were characterized in rats receiving a 30-min infusion of ShenMai at 10 mL/kg. Inhibition of human OATP1B1/1B3 and rat Oatp1b2 by the individual saponins was investigated in vitro; the compounds' joint inhibition was also assessed in vitro and the data was processed using the Chou-Talalay method. Plasma protein binding was assessed by equilibrium dialysis. Altogether, 49 saponins in ShenMai were characterized and graded into: 10-100 μmol/day (compound doses from ShenMai; 7 compounds), 1-10 μmol/day (17 compounds), and <1 μmol/day (25 compounds, including Maidong ophiopogonins). After dosing, circulating saponins were protopanaxadiol-type ginsenosides Rb₁, Rb₂, Rc, Rd, Ra₁, Rg₃, Ra₂, and Ra₃, protopanaxatriol-type ginsenosides Rg₁, Re, Rg₂, and Rf, and ginsenoside Ro. The protopanaxadiol-type ginsenosides exhibited maximum plasma concentrations of 2.1-46.6 μmol/L, plasma unbound fractions of 0.4-1.0% and terminal half-lives of 15.6-28.5 h (ginsenoside Rg₃, 1.9 h), while the other ginsenosides exhibited 0.1-7.7 μmol/L, 20.8-99.2%, and 0.2-0.5 h, respectively. The protopanaxadiol-type ginsenosides, ginsenosides without any sugar attachment at C-20 (except ginsenoside Rf), and ginsenoside Ro inhibited OATP1B3 more potently (IC₅₀, 0.2-3.5 µmol/L) than the other ginsenosides (≥22.6 µmol/L). Inhibition of OATP1B1 by ginsenosides was less potent than OATP1B3 inhibition. Ginsenosides Rb₁, Rb₂, Rc, Rd, Ro, Ra₁, Re, and Rg₂ likely contribute the major part of OATP1B3-mediated ShenMai-drug interaction potential, in an additive and time-related manner.

Better prediction of the local concentration-effect relationship: the role of physiologically based pharmacokinetics and quantitative systems pharmacology and toxicology in the evolution of model-informed drug discovery and development

Model-informed drug discovery and development (MID3) is an umbrella term under which sit several computational approaches: quantitative systems pharmacology (QSP), quantitative systems toxicology (QST) and physiologically based pharmacokinetics (PBPK). QSP models are built using mechanistic knowledge of the pharmacological pathway focusing on the putative mechanism of drug efficacy; whereas QST models focus on safety and toxicity issues and the molecular pathways and networks that drive these adverse effects. These can be mediated through exaggerated on-target or off-target pharmacology, immunogenicity or the physiochemical nature of the compound. PBPK models provide a mechanistic description of individual organs and tissues to allow the prediction of the intra- and extra-cellular concentration of the parent drug and metabolites under different conditions. Information on biophase concentration enables the prediction of a drug effect in different organs and assessment of the potential for drug-drug interactions. Together, these modelling approaches can inform the exposure-response relationship and hence support hypothesis generation and testing, compound selection, hazard identification and risk assessment through to clinical proof of concept (POC) and beyond to the market.

Effects of proton pump inhibitors on severe haematotoxicity induced after first course of pemetrexed/carboplatin combination chemotherapy

WHAT IS KNOWN AND OBJECTIVE

Pemetrexed/carboplatin combination chemotherapy has shown efficacy as a first-line treatment for advanced non-small-cell lung cancer. However, severe haematotoxicity is often observed during this combination chemotherapy. Some studies have suggested that concomitant drugs may be the risk factors for severe adverse events. However, those studies identified the predictive risk factors without paying attention to the relative dose intensities (RDIs) of the anticancer drugs. The objective of this study was to clarify the effects of concomitant drugs on the severe haematotoxicity induced by pemetrexed/carboplatin combination chemotherapy using multiple logistic regression analysis incorporating RDIs of the anticancer drugs.

METHODS

We retrospectively reviewed the records of 61 patients who had received first-line treatment with this combination chemotherapy at Yamato Municipal Hospital between April 2011 and May 2017. Severe haematotoxicity was defined as grade 3 or 4 according to the Common Terminology Criteria for Adverse Events, version 4.0. To clarify the influence of concomitant drugs on haematotoxicity, we performed multiple logistic regression analysis.

RESULTS

Among the 61 patients, 18 (29.5%) developed grade 3 or 4 haematotoxicity. Multiple logistic regression analysis showed that body weight <54.5 kg [odds ratio: 5.21, 95% confidence interval (CI): 1.17-23.08, P = 0.030], haemoglobin <12.0 g/dL [odds ratio: 7.13, 95% CI: 1.54-33.11, P = 0.012], and coadministration of proton pump inhibitors (PPIs) [odds ratio: 5.34, 95% CI: 1.06-26.94, P = 0.042] were significantly associated with severe haematotoxicity in patients receiving pemetrexed/carboplatin combination chemotherapy after adjustment using non-steroidal anti-inflammatory drugs and RDIs of the anticancer drugs.

WHAT IS NEW AND CONCLUSION

Multiple logistic regression analysis incorporating RDIs of the anticancer drugs revealed that low baseline body weight, low baseline haemoglobin level, and coadministration of PPIs were the independent risk factors for predicting severe haematotoxicity induced by pemetrexed/carboplatin combination chemotherapy.

Advancing Predictions of Tissue and Intracellular Drug Concentrations Using In Vitro, Imaging and Physiologically Based Pharmacokinetic Modeling Approaches

This white paper examines recent progress, applications, and challenges in predicting unbound and total tissue and intra/subcellular drug concentrations using in vitro and preclinical models, imaging techniques, and physiologically based pharmacokinetic (PBPK) modeling. Published examples, regulatory submissions, and case studies illustrate the application of different types of data in drug development to support modeling and decision making for compounds with transporter-mediated disposition, and likely disconnects between tissue and systemic drug exposure. The goals of this article are to illustrate current best practices and outline practical strategies for selecting appropriate in vitro and in vivo experimental methods to estimate or predict tissue and plasma concentrations, and to use these data in the application of PBPK modeling for human pharmacokinetic (PK), efficacy, and safety assessment in drug development.

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.

Glycyrrhizin has a high likelihood to be a victim of drug-drug interactions mediated by hepatic organic anion-transporting polypeptide 1B1/1B3

BACKGROUND AND PURPOSE

Intravenous glycyrrhizin, having anti-inflammatory and hepatoprotective properties, is incorporated into the management of liver diseases in China. This investigation was designed to elucidate the molecular mechanism underlying hepatobiliary excretion of glycyrrhizin and to investigate its potential for drug-drug interactions on organic anion-transporting polypeptide (OATP)1B.

EXPERIMENTAL APPROACH

Human transporters mediating hepatobiliary excretion of glycyrrhizin were characterized at the cellular and vesicular levels and compared with rat hepatic transporters. The role of Oatp1b2 in glycyrrhizin's elimination and pharmacokinetics was evaluated in rats using the inhibitor rifampin. A physiologically based pharmacokinetic (PBPK) model for glycyrrhizin, incorporating transporter-mediated hepatobiliary excretion, was established and applied to predict potential drug-drug interactions related to glycyrrhizin in humans.

KEY RESULTS

Hepatobiliary excretion of glycyrrhizin involved human OATP1B1/1B3 (Oatp1b2 in rats)-mediated hepatic uptake from blood and human multidrug resistance-associated protein (MRP)2/breast cancer resistance protein (ABCP)/bile salt export pump (BSEP)/multidrug resistance protein 1 (Mrp2/Abcp/Bsep in rats)-mediated hepatic efflux into bile. In rats, rifampin impaired hepatic uptake of glycyrrhizin significantly increasing its systemic exposure. Glomerular-filtration-based renal excretion of glycyrrhizin was slow due to extensive protein binding in plasma. Quantitative analysis using the PBPK model demonstrated that OATP1B1/1B3 have critical roles in the pharmacokinetics of glycyrrhizin, which is highly likely to be a victim of drug-drug interactions when co-administered with potent dual inhibitors of these transporters.

CONCLUSIONS AND IMPLICATIONS

Transporter-mediated hepatobiliary excretion governs glycyrrhizin's elimination and pharmacokinetics. Understanding glycyrrhizin's potential drug-drug interactions on OATP1B1/1B3 should enhance the therapeutic outcome of glycyrrhizin-containing drug combinations on liver diseases.

Utility of physiologically based pharmacokinetic (PBPK) modeling in oncology drug development and its accuracy: a systematic review

PURPOSE

Physiologically based pharmacokinetic (PBPK) modeling, a mathematical modeling approach which uses a pharmacokinetic model to mimick human physiology to predict drug concentration-time profiles, has been used for the discover and development of drugs in various fields, including oncology, since 2000. There have been a few general review articles on the utilization of PBPK in the development of oncology drugs, but these do not include an evaluation of model prediction accuracy. We therefore conducted a systematic review to define the accuracy of PBPK model prediction and its utility throughout all the developmental phases of oncology drugs.

METHODS

A systematic search was performed in the PubMed, PubMed Central and Cochrane Library databases from 1980 to February 2017 for articles (1) written in English, (2) focused on oncology or antineoplastic or anticancer drugs, tumor or cancer or anticancer drugs listed in the U.S. National Institutes of Health and (3) involving a PBPK model. The absolute-average-folding-errors (AAFEs) of the area under the curve (AUC) between predicted and observed values in each article were calculated to assess model prediction accuracy.

RESULTS

Of the 2341 articles initially identified by our search of the databases, 40 were included in the review analysis. These articles reported on six types of studies, i.e. in vivo (n = 4), first-in-human (n = 5), phase II/III clinical trials (n = 9), organ impairment (n = 3), pediatrics (n = 4) and drug-drug interactions (n = 15). AAFEs of the predicted AUC for all groups of studies were within 1.3-fold of each other despite variations in experimental methodologies.

CONCLUSION

PBPK modeling is a potential tool which can be effectively applied throughout all phases of oncology drug development. The number of experimental animals and human participants enrolled in the studies can be reduced using PBPK modeling and PBPK-population-PK modeling. The limited number of publications of unsuccessful model application to date may contribute to bias toward the usefulness of modeling.

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.

Renal Drug Transporters and Drug Interactions

Transporters in proximal renal tubules contribute to the disposition of numerous drugs. Furthermore, the molecular mechanisms of tubular secretion have been progressively elucidated during the past decades. Organic anions tend to be secreted by the transport proteins OAT1, OAT3 and OATP4C1 on the basolateral side of tubular cells, and multidrug resistance protein (MRP) 2, MRP4, OATP1A2 and breast cancer resistance protein (BCRP) on the apical side. Organic cations are secreted by organic cation transporter (OCT) 2 on the basolateral side, and multidrug and toxic compound extrusion (MATE) proteins MATE1, MATE2/2-K, P-glycoprotein, organic cation and carnitine transporter (OCTN) 1 and OCTN2 on the apical side. Significant drug-drug interactions (DDIs) may affect any of these transporters, altering the clearance and, consequently, the efficacy and/or toxicity of substrate drugs. Interactions at the level of basolateral transporters typically decrease the clearance of the victim drug, causing higher systemic exposure. Interactions at the apical level can also lower drug clearance, but may be associated with higher renal toxicity, due to intracellular accumulation. Whereas the importance of glomerular filtration in drug disposition is largely appreciated among clinicians, DDIs involving renal transporters are less well recognized. This review summarizes current knowledge on the roles, quantitative importance and clinical relevance of these transporters in drug therapy. It proposes an approach based on substrate-inhibitor associations for predicting potential tubular-based DDIs and preventing their adverse consequences. We provide a comprehensive list of known drug interactions with renally-expressed transporters. While many of these interactions have limited clinical consequences, some involving high-risk drugs (e.g. methotrexate) definitely deserve the attention of prescribers.

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.

Correlation between Apparent Substrate Affinity and OCT2 Transport Turnover

Organic cation (OC) transporter 2 (OCT2) mediates the first step in the renal secretion of many cationic drugs: basolateral uptake from blood into proximal tubule cells. The impact of this process on the pharmacokinetics of drug clearance as estimated using a physiologically-based pharmacokinetic approach relies on an accurate understanding of the kinetics of transport because the ratio of the maximal rate of transport to the Michaelis constant (i.e., J_max/ K_t) provides an estimate of the intrinsic clearance (Cl_int) used in in vitro-in vivo extrapolation of experimentally determined transport data. Although the multispecificity of renal OC secretion, including that of the OCT2 transporter, is widely acknowledged, the possible relationship between relative affinity of the transporter for its diverse substrates and the maximal rates of their transport has received little attention. In this study, we determined the J_max and apparent Michaelis constant (K_tapp) values for six structurally distinct OCT2 substrates and found a strong correlation between J_max and K_tapp; high-affinity substrates [K_tapp values <50 µM, including 1-methyl-4-phenylpyridinium, or 1-methyl-4-phenylpyridinium (MPP), and cimetidine] displayed systematically lower J_max values (<50 pmol cm^-2 min^-1) than did low-affinity substrates (K_tapp >200 µM, including choline and metformin). Similarly, preloading OCT2-expressing cells with low-affinity substrates resulted in systematically larger trans-stimulated rates of MPP uptake than did preloading with high-affinity substrates. The data are quantitatively consistent with the hypothesis that dissociation of bound substrate from the transporter is rate limiting in establishing maximal rates of OCT2-mediated transport. This systematic relationship may provide a means to estimate Cl_int for drugs for which transport data are lacking.

Prediction of renal transporter-mediated drug-drug interactions for a drug which is an OAT substrate and inhibitor using PBPK modelling

A PBPK modelling approach was used to predict organic anion transporter (OAT) mediated drug-drug interactions involving S44121, a substrate and an inhibitor of OAT1 and OAT3. Model predictions were then compared to the results of a clinical DDI study which was carried out to investigate the interaction of S44121 with probenecid, tenofovir and ciprofloxacin. PBPK models were developed and qualified using existing clinical data, and inhibition constants were determined in vitro. The model predictions for S44121 as an OAT inhibitor were similar to the results obtained from the clinical DDI study, with no interaction observed for tenofovir or ciprofloxacin in the presence of S44121. An observed AUC ratio of 2.2 was obtained for S44121 in the presence of probenecid, which was slightly higher than the model predicted AUC ratio of 1.6. A DDI study in the monkey was also carried out for the interaction between S44121 and probenecid, since the monkey has previously been reported to be a good preclinical model for OAT-mediated DDI. However, this study highlighted a species difference in the major route of S44121 elimination between monkey (mainly hepatic metabolism) and human (mainly renal excretion of unchanged drug), rendering a comparison between the two DDI studies difficult. Overall, for S44121 the PBPK modelling approach gave a better prediction of the extent of DDI than the static predictions based on inhibitor C_max and IC₅₀, therefore this can be considered a potentially valuable tool within drug development.

Non-steroidal anti-inflammatory drugs induce severe hematologic toxicities in lung cancer patients receiving pemetrexed plus carboplatin: A retrospective cohort study

Purpose

As the major toxicity induced by pemetrexed plus carboplatin is severe hematologic toxicities, the aim of this study was to determine the risk factors for severe hematologic toxicities in lung cancer patients.

Methods

We retrospectively investigated data from lung cancer patients who had received pemetrexed plus carboplatin, with or without bevacizumab. This observational study was carried out at Ehime University Hospital using electronic medical records dating from July 2009 to March 2015. Severe hematologic toxicities were defined as grade 3 or 4, according to the Common Terminology Criteria for Adverse Events, version 4.0.

Results

Forty-two patients were included in the study. The incidence of grade 3 or 4 hematologic toxicities during the first cycle of chemotherapy and during all cycles was 19.0% and 16.1%, respectively. Multivariate time-depend generalized estimating equations logistic regression analysis revealed that regular use of non-steroidal anti-inflammatory drugs (NSAIDs) was significantly associated with an increased risk of severe hematologic toxicities during all cycles (adjusted odds ratio (OR): 8.32, 95% confidence interval (CI): 1.27–54.38; p = 0.03), whereas creatinine clearance of <45 mL/min was not significantly associated with an increased risk of severe hematologic toxicities during all cycles (adjusted OR: 0.91, 95% CI: 0.25–3.34; p = 0.88).

Conclusions

The results suggest that severe hematologic toxicities in patients receiving carboplatin-based pemetrexed may be significantly induced by the inhibition of renal tubular pemetrexed secretion through drug–drug interactions between NSAIDs and pemetrexed rather than through glomerular filtration of pemetrexed, even with moderate to sufficient renal function.

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 (CL_R) 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 CL_R, 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.

Unequivocal evidence supporting the segregated flow intestinal model that discriminates intestine versus liver first-pass removal with PBPK modeling

Merits of the segregated flow model (SFM), highlighting the intestine as inert serosa and active enterocyte regions, with a smaller fractional (f_Q < 0.3) intestinal flow (Q_I ) perfusing the enterocyte region, are described. Less drug in the circulation reaches the enterocytes due to the lower flow (f_Q Q_I ) in comparison with drug administered into the gut lumen, fostering the idea of route-dependent intestinal removal. The SFM has been found superior to the traditional model (TM), which views the serosa and enterocytes totally as a well-mixed tissue perfused by 100% of the intestinal flow, Q_I . The SFM model is able to explain the lower extents of intestinal metabolism of enalapril, morphine and midazolam with i.v. vs. p.o. dosing. For morphine, the urine/bile ratio of the metabolite, morphine glucuronide MGurineMGbile for p.o. was 2.6× that of i.v. This was due to the higher proportion of intestinally formed morphine glucuronide, appearing more in urine than in bile due to its low permeability and greater extent of intestinal formation with p.o. administration. By contrast, the TM predicted the same MGurineMGbile for p.o. vs. i.v. The TM predicted that the contributions of the intestine:liver to first-pass removal were 46%:54% for both p.o. and i.v. The SFM predicted same 46%:54% (intestine:liver) for p.o., but 9%:91% for i.v. By contrast, the kinetics of codeine, the precursor of morphine, was described equally well by the SFM- and TM-PBPK models, a trend suggesting that intestinal metabolism of codeine is negligible. Fits to these PBPK models further provide insightful information towards metabolite formation: available fractions and the fractions of hepatic and total clearances that form the metabolite in question. The SFM-PBPK model is useful to identify not only the presence of intestinal metabolism but the contributions of the intestine and liver for metabolite formation. Copyright © 2016 John Wiley & Sons, Ltd.

Novel minimal physiologically-based model for the prediction of passive tubular reabsorption and renal excretion clearance

PURPOSE

Develop a minimal mechanistic model based on in vitro-in vivo extrapolation (IVIVE) principles to predict extent of passive tubular reabsorption. Assess the ability of the model developed to predict extent of passive tubular reabsorption (Freab) and renal excretion clearance (CLR) from in vitro permeability data and tubular physiological parameters.

METHODS

Model system parameters were informed by physiological data collated following extensive literature analysis. A database of clinical CLR was collated for 157 drugs. A subset of 45 drugs was selected for model validation; for those, Caco-2 permeability (Papp) data were measured under pH6.5-7.4 gradient conditions and used to predict Freab and subsequently CLR. An empirical calibration approach was proposed to account for the effect of inter-assay/laboratory variation in Papp on the IVIVE of Freab.

RESULTS

The 5-compartmental model accounted for regional differences in tubular surface area and flow rates and successfully predicted the extent of tubular reabsorption of 45 drugs for which filtration and reabsorption were contributing to renal excretion. Subsequently, predicted CLR was within 3-fold of the observed values for 87% of drugs in this dataset, with an overall gmfe of 1.96. Consideration of the empirical calibration method improved overall prediction of CLR (gmfe=1.73 for 34 drugs in the internal validation dataset), in particular for basic drugs and drugs with low extent of tubular reabsorption.

CONCLUSIONS

The novel 5-compartment model represents an important addition to the IVIVE toolbox for physiologically-based prediction of renal tubular reabsorption and CLR. Physiological basis of the model proposed allows its application in future mechanistic kidney models in preclinical species and human.

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.

Abundance of Hepatic Transporters in Caucasians: A Meta-Analysis

This study aimed to derive quantitative abundance values for key hepatic transporters suitable for in vitro–in vivo extrapolation within a physiologically based pharmacokinetic modeling framework. A meta-analysis was performed whereby data on abundance measurements, sample preparation methods, and donor demography were collated from the literature. To define values for a healthy Caucasian population, a subdatabase was created whereby exclusion criteria were applied to remove samples from non-Caucasian individuals, those with underlying disease, or those with subcellular fractions other than crude membrane. Where a clinically relevant active genotype was known, only samples from individuals with an extensive transporter phenotype were included. Authors were contacted directly when additional information was required. After removing duplicated samples, the weighted mean, geometric mean, standard deviation, coefficient of variation, and between-study homogeneity of transporter abundances were determined. From the complete database containing 24 transporters, suitable abundance data were available for 11 hepatic transporters from nine studies after exclusion criteria were applied. Organic anion transporting polypeptides OATP1B1 and OATP1B3 showed the highest population abundance in healthy adult Caucasians. For several transporters, the variability in abundance was reduced significantly once the exclusion criteria were applied. The highest variability was observed for OATP1B3 > OATP1B1 > multidrug resistance protein 2 > multidrug resistance gene 1. No relationship was found between transporter expression and donor age. To our knowledge, this study provides the first in-depth analysis of current quantitative abundance data for a wide range of hepatic transporters, with the aim of using these data for in vitro–in vivo extrapolation, and highlights the significance of investigating the background of tissue(s) used in quantitative transporter proteomic studies. Similar studies are now warranted for other ethnicities.

Lansoprazole Exacerbates Pemetrexed-Mediated Hematologic Toxicity by Competitive Inhibition of Renal Basolateral Human Organic Anion Transporter 3

Pemetrexed, a multitargeted antifolate, is eliminated by tubular secretion via human organic anion transporter 3 (hOAT3). Although proton pump inhibitors (PPIs) are frequently used in cancer patients, the drug interaction between PPIs and pemetrexed remains to be clarified. In this study, we examined the drug interaction between pemetrexed and PPIs in hOAT3-expressing cultured cells, and retrospectively analyzed the impact of PPIs on the development of hematologic toxicity in 108 patients who received pemetrexed and carboplatin treatment of nonsquamous non-small cell lung cancer for the first time between January 2011 and June 2015. We established that pemetrexed was transported via hOAT3 (Km = 68.3 ± 11.1 µM). Lansoprazole, rabeprazole, pantoprazole, esomeprazole, omeprazole, and vonoprazan inhibited hOAT3-mediated uptake of pemetrexed in a concentration-dependent manner. The inhibitory effect of lansoprazole was much greater than those of other PPIs and the apparent IC50 value of lansoprazole against pemetrexed transport via hOAT3 was 0.57 ± 0.17 µM. The inhibitory type of lansoprazole was competitive. In a retrospective study, multivariate analysis revealed that coadministration of lansoprazole, but not other PPIs, with pemetrexed and carboplatin was an independent risk factor significantly contributing to the development of hematologic toxicity (odds ratio: 10.004, P = 0.005). These findings demonstrated that coadministration of lansoprazole could exacerbate the hematologic toxicity associated with pemetrexed, at least in part, by competitive inhibition of hOAT3. Our results would aid clinicians to make decisions of coadministration drugs to avoid drug interaction-induced side effects for achievement of safe and appropriate chemotherapy with pemetrexed.

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.

Effect of renal function on pemetrexed-induced haematotoxicity

PURPOSE

Pemetrexed (PEM) is an anticancer agent used for the treatment of non-small cell lung cancer, malignant pleural mesothelioma and thymoma. Reportedly, PEM has higher efficacy and safety when used in combination with platinum-based agents. However, there are only few reports on the safety of PEM in patients with an eGFR of ≤45 mL/min. We examined the effect of renal function on the safety of regimens containing PEM.

METHODS

We retrospectively reviewed 221 patients with lung cancer, malignant pleural mesothelioma or thymoma who received treatment with a PEM-containing regimen between 2009 and 2014. Subgroup analyses were performed on the basis of pre-treatment renal function: group A [creatinine clearance (CLcr), <45 mL/min]; group B (CLcr, 45-80 mL/min); and group C (CLcr, ≥80 mL/min). For the purpose of this analysis, the lowest documented blood cell counts and haemoglobin levels, the highest levels of serum creatinine, aspartate aminotransferase, alanine aminotransferase and CLcr from the time of initial administration up to prior to the start of second administration were considered.

RESULTS

Groups A, B and C had 8, 123 and 90 patients, respectively. The incidence of grade 2 thrombocytopaenia was significantly higher in group A as compared to that in groups B (P < 0.01) and C (P < 0.05). On multivariate analysis, only a CLcr of <45 mL/min was an independent risk factor for thrombocytopaenia of ≥grade 2.

CONCLUSION

When administering a PEM-containing regimen, thrombocytopaenia of ≥grade 2 is more likely to develop in patients with a CLcr of <45 mL/min.

Pemetrexed-Induced Nephrogenic Diabetes Insipidus

Pemetrexed is an approved antimetabolite agent, now widely used for treating locally advanced or metastatic nonsquamous non-small cell lung cancer. Although no electrolyte abnormalities are described in the prescribing information for this drug, several case reports have noted nephrogenic diabetes insipidus with associated acute kidney injury. We present a case of nephrogenic diabetes insipidus without severely reduced kidney function and propose a mechanism for the isolated finding. Severe hypernatremia can lead to encephalopathy and osmotic demyelination, and our report highlights the importance of careful monitoring of electrolytes and kidney function in patients with lung cancer receiving pemetrexed.

Mechanisms of nonsteroidal anti-inflammatory drugs in cancer prevention

Various clinical and epidemiologic studies show that nonsteroidal anti-inflammatory drugs (NSAIDs), including aspirin and cyclooxygenase inhibitors (COXIBs) help prevent cancer. Since eicosanoid metabolism is the main inhibitory targets of these drugs the resulting molecular and biological impact is generally accepted. As our knowledge base and technology progress we are learning that additional targets may be involved. This review attempts to summarize these new developments in the field.