Quantitative analysis of elevation of serum creatinine via renal transporter inhibition by trimethoprim in healthy subjects using physiologically-based pharmacokinetic model

Clinical Assessment of Drug Transporter Inhibition Using Biomarkers: Review of the Literature (2015-2024)

As part of a narrative review of various publications describing the clinical use of urine- and plasma-based drug transporter biomarkers, it was determined that the utilization of coproporphyrin I, a hepatic organic anion transporting polypeptide (OATP) 1B1 and OATP1B3 biomarker, has been reported for 28 different drug-drug interaction (DDI) perpetrator drugs. Similarly, biomarkers for liver organic cation transporter 1 (isobutyryl-l-carnitine, N = 7 inhibitors), renal organic cation transporter 2 and multidrug and toxin extrusion proteins (N1-methylnicotinamide, N = 13 inhibitors), renal organic anion transporter (OAT) 1 and 3 (pyridoxic acid, N = 7 inhibitors), and breast cancer resistance protein (riboflavin, N = 3 inhibitors) have also been described. Increased use of biomarkers has also been accompanied by modeling efforts to enable DDI predictions and development of multiplexed methods to facilitate their bioanalysis. Overall, there is consensus that exploratory biomarkers such as coproporphyrin I can be integrated into decision trees encompassing in vitro transporter inhibition data, DDI risk assessments, and follow-up Phase 1 studies. Therefore, sponsors can leverage biomarkers to evaluate dose-dependent inhibition of selected transporters, use them jointly with drug probes to deconvolute DDI mechanisms, and integrate in vitro data packages to establish calibrated (biomarker informed) DDI risk assessment cutoffs. Although transporter biomarker science has progressed, reflected by its inclusion in the recently issued International Council for Harmonisation DDI guidance document (M12), some biomarkers still require further validation. There is also a need for biomarkers that can differentiate specific transporters (e.g., OATP1B3 vs OATP1B1 and OAT1 vs OAT3).

Quantitative Consideration of Clinical Increases in Serum Creatinine Caused by Renal Transporter Inhibition

Creatinine is a common biomarker of renal function and is secreted in the renal tubular cells via drug transporters, such as organic cation transporter 2 and multidrug and toxin extrusion (MATE) 1/2-K. To differentiate between drug-induced acute kidney injury (AKI) and drug interactions through the renal transporter, it has been examined whether these transporter inhibitions quantitatively explained increases in serum creatinine (SCr) at their clinically relevant concentrations using drugs without any changes in renal function. For such renal transporter inhibitors and recently approved tyrosine kinase inhibitors (TKIs), this mini-review describes clinical increases in SCr and inhibitory potentials against the renal transporters. Most cases of SCr elevations can be explained by considering the renal transporter inhibitions based on unbound maximum plasma concentrations, except for drugs associated with obvious changes in renal function. SCr increases for cobicistat, dolutegravir, and dronedarone, and some TKIs were significantly underestimated, and these underestimations were suggested to be associated with low plasma unbound fractions. Sensitivity analysis of SCr elevations regarding inhibitory potentials of MATE1/2-K demonstrated that typical inhibitors such as cimetidine, DX-619, pyrimethamine, and trimethoprim could give false interpretations of AKI according to the criteria based on relative or absolute levels of SCr elevations. Recent progress and current challenges of physiologically-based pharmacokinetics modeling for creatinine disposition were also summarized. Although it should be noted for the potential impact of in vitro assay designs on clinical translatability of transporter inhibitions data, mechanistic approaches could support decision-making in clinical development to differentiate between AKI and creatinine-drug interactions. SIGNIFICANCE STATEMENT: Serum creatinine (SCr) is widely used as an indicator of kidney function, but it increases due to inhibitions of renal transporters, such as multidrug and toxin extrusion protein 1/2-K despite no functional changes in the kidney. Such SCr elevations were quantitatively explained by renal transporter inhibitions except for some drugs with high protein binding. The present analysis demonstrated that clinically relevant inhibitors of the renal transporters could cause SCr elevations above levels corresponding to acute kidney injury criteria.

Impact of Direction of Transport on the Evaluation of Substrate Recognition of Mouse Multidrug and Toxin Extrusion Protein 1

Multidrug and toxin extrusion protein (MATE/SLC47A) secretes metabolites and xenobiotics into the urine in the proximal tubules of the kidney. Uptake assays have been commonly used for evaluating MATE-mediated transport of new chemical entities in drug development. The purpose of this study was to examine the relationship between in vitro uptake activities by MATEs and the impact of MATE-mediated transport in in vivo renal secretion. In vitro uptake in mouse Mate1 (mMate1)-expressing human embryonic kidney 293 (HEK293) cells and several in vivo parameters from mMate1 knockout and wild-type mice were compared using nine cationic compounds (almotriptan, naratriptan, talinolol, sumatriptan, alogliptin, sitagliptin, rivaroxaban, saxagliptin, and vildagliptin). Compounds that showed statistically significant decrease in secretory clearances with respect to kidney concentrations (CLR,kidney) in mMate1 knockout mice were categorized as in vivo substrates in this study. A good correlation (R2 = 0.637) was observed between the in vitro uptake ratio and the in vivo ratio of CLR,kidney of mMate1 knockout mice and wild-type mice. This study supported the rationale of using an uptake assay to determine whether investigational compounds are the substrate of MATEs and to predict drug-drug interaction risk via renal secretion by MATE from the viewpoint of drug development in pharmaceutical companies. SIGNIFICANCE STATEMENT: We revealed that substrates judged by in vitro experiments using mouse multidrug and toxin extrusion (mMate)1-expressing cells were excreted in urine via mMate1 in vivo, and a good correlation (R2 = 0.637) was observed between in vitro uptake ratio and in vivo ratio of secretory clearance with respect to the kidney concentrations (CLR,kidney) of mMate1 knockout and wild-type mice. This study supported the rationale of using an uptake assay to predict potential human MATE1-mediated drug-drug interaction as a victim.

Challenges and Opportunities for Improved Drug-Drug Interaction Predictions for Renal OCT2 and MATE1/2-K Transporters

Transporters contribute to renal elimination of drugs; therefore drug disposition can be impacted if transporters are inhibited by comedicant drugs. Regulatory agencies have provided guidelines to assess potential drug-drug interaction (DDI) risk for renal organic cation transporter 2 (OCT2) and multidrug and toxin extrusion 1 and 2-K (MATE1/2-K) transporters. Despite this, there are challenges with translating in vitro data using currently available tools to obtain a quantitative assessment of DDI risk in the clinic. Given the high number of drugs and new molecular entities showing in vitro inhibition toward OCT2 and/or MATE1/2-K and the lack of translation to clinically significant effects, it is reasonable to question whether the current in vitro assay design and modeling practice has led to unnecessary clinical evaluation. The aim of this review is to assess and discuss available in vitro and clinical data along with prediction models intended to provide clinical context of risk, including static models proposed by regulatory agencies and physiologically-based pharmacokinetic models, in order to identify best practices and areas of future opportunity. This analysis highlights that different in vitro assay designs, including substrate and cell systems used, strongly influence the derived concentration of drug producing 50% inhibition values and contribute to high variability observed across laboratories. Furthermore, the lack of sensitive index substrates coupled with specific inhibitors for individual transporters necessitates the use of complex models to evaluate clinical DDI risk.

Impact of trimethoprim on serum creatinine, sodium, and potassium concentrations in patients taking trimethoprim-sulfamethoxazole without changes in glomerular filtration rate

WHAT IS KNOWN AND OBJECTIVE

Trimethoprim (TMP) inhibits the Na⁺ /K⁺ -ATPase present in the basement membrane of distal tubular epithelial cells. However, hyponatremia and hyperkalemia may develop in patients taking TMP-sulfamethoxazole (SMX). In addition, because TMP inhibits drug transporters, such as organic cation transporter 2 and multidrug and toxin extrusion protein 2-K in proximal tubules, reversible increases in the concentration of serum creatinine (SCr), the substrate of these transporters, may occur. Here, we investigated variability in SCr, serum sodium (Na⁺ ), and serum potassium (K⁺ ) concentrations after initiation of TMP-SMX treatment and evaluated the risk of hyponatremia and hyperkalemia in patients with increased SCr concentrations without changes in the glomerular filtration rate (GFR).

METHODS

In this retrospective study, all patients aged 20 years or older who received oral TMP-SMX during hospitalization were enrolled. The patients with estimated creatinine (Cr) clearance (eCCr) lower than 30 mL/min were excluded, as were patients taking drugs that were likely to induce renal dysfunction, drugs other than glucocorticoids that were likely to induce electrolyte imbalances, or drugs other than TMP that inhibit tubular Cr secretion. Additionally, those with SCr concentrations elevated more than 30% from baseline or serum blood urea nitrogen concentration levels above 20 mg/dL during follow-up were also excluded.

RESULTS AND DISCUSSION

In total, 111 patients were enrolled in the study. The common independent variable affecting the change rate in SCr, Na⁺ , and K⁺ concentrations (ΔSCr, ΔNa⁺ , and ΔK⁺ ) from baseline to the highest value during the follow-up period (14 days after initiation of TMP-SMX treatment) was the daily dose of TMP. There were significant correlations between ΔSCr and ΔNa⁺ or ΔK⁺ (ρ = -0.199, p = 0.036 and ρ = 0.244, p = 0.010, respectively). Kaplan-Meier curves for hyponatremia and hyperkalemia with greater than or equal to grade 1 severity showed different profiles when the TMP dose varied (≤ 160 vs. > 160 mg/day; p = 0.005 and 0.008). The cumulative incidences of both adverse effects were 64.7% (median: 7 days) and 29.4% in patients taking more than 160 mg/day TMP and 35.2% and 6.7% in patients taking 160 mg/day TMP or less. Thus, TMP may affect the kinetics of Cr, Na⁺ , and K⁺ in the proximal and distal tubules in a dose-dependent without changing the GFR.

WHAT IS NEW AND CONCLUSION

This study is the first report to demonstrate the degree of changes in SCr, Na⁺ , and K⁺ concentrations after initiation of TMP-SMX treatment. If SCr is elevated after initiation of TMP-SMX treatment, clinicians should be aware of decreased Na⁺ and increased K⁺ concentrations. TMP may increase the risks of hyponatremia and hyperkalemia in a dose-dependent manner without altering GFR.

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

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

Impact of fedratinib on the pharmacokinetics of transporter probe substrates using a cocktail approach

INTRODUCTION

Fedratinib, an oral, selective Janus kinase 2 inhibitor, has been shown to inhibit P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), organic anion transporting polypeptide (OATP) 1B1, OATP1B3, organic cation transporter (OCT) 2, and multidrug and toxin extrusion (MATE) 1 and MATE2-K in vitro. The objective of this study was to evaluate the influence of fedratinib on the pharmacokinetics (PK) of digoxin (P-gp substrate), rosuvastatin (OATP1B1/1B3 and BCRP substrate), and metformin (OCT2 and MATE1/2-K substrate).

METHODS

In this nonrandomized, fixed-sequence, open-label study, 24 healthy adult participants received single oral doses of digoxin 0.25 mg, rosuvastatin 10 mg, and metformin 1000 mg administered as a drug cocktail (day 1, period 1). After a 6-day washout, participants received oral fedratinib 600 mg 1 h before the cocktail on day 7 (period 2). An oral glucose tolerance test (OGTT) was performed to determine possible influences of fedratinib on the antihyperglycemic effect of metformin.

RESULTS

Plasma exposure to the three probe drugs was generally comparable in the presence or absence of fedratinib. Reduced metformin renal clearance by 36% and slightly higher plasma glucose levels after OGTT were observed in the presence of fedratinib. Single oral doses of the cocktail ± fedratinib were generally well tolerated.

CONCLUSIONS

These results suggest that fedratinib has minimal impact on the exposure of P-gp, BCRP, OATP1B1/1B3, OCT2, and MATE1/2-K substrates. Since renal clearance of metformin was decreased in the presence of fedratinib, caution should be exercised in using coadministered drugs that are renally excreted via OCT2 and MATEs.

TRIAL REGISTRATION

Clinicaltrials.gov NCT04231435 on January 18, 2020.

Development and verification of an endogenous PBPK model to inform hydrocortisone replacement dosing in children and adults with cortisol deficiency

The goal of hormone replacement is to mirror physiology. Hydrocortisone granules and modified release formulations are being developed to optimise cortisol replacement in the rare disease of adrenal insufficiency. To facilitate clinical development, we built and verified a physiologically based pharmacokinetic (PBPK) model for the endogenous hormone cortisol (hydrocortisone) in healthy adults, and children and adults with adrenal insufficiency. The model predicted immediate-release hydrocortisone pharmacokinetics in adults across the dose range 0.5 to 20 mg, with predicted/observed AUCs within 0.8 to 1.25-fold.  The model also tightly predicted pharmacokinetic parameters for modified-release formulations, with AUCs within 0.8 to 1.25-fold after single and multiple dosing.  Predicted modified-release formulation pharmacokinetics (PK) in 12 to 18-year olds showed PK to be similar to adults. This hydrocortisone PBPK model is a useful tool to predict adult and paediatric pharmacokinetics of both immediate- and modified-release hydrocortisone formulations, and develop clinical dosing regimens.

A Whole-Body Physiologically Based Pharmacokinetic Model Characterizing Interplay of OCTs and MATEs in Intestine, Liver and Kidney to Predict Drug-Drug Interactions of Metformin with Perpetrators

Transmembrane transport of metformin is highly controlled by transporters including organic cation transporters (OCTs), plasma membrane monoamine transporter (PMAT), and multidrug/toxin extrusions (MATEs). Hepatic OCT1, intestinal OCT3, renal OCT2 on tubule basolateral membrane, and MATE1/2-K on tubule apical membrane coordinately work to control metformin disposition. Drug–drug interactions (DDIs) of metformin occur when co-administrated with perpetrators via inhibiting OCTs or MATEs. We aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model characterizing interplay of OCTs and MATEs in the intestine, liver, and kidney to predict metformin DDIs with cimetidine, pyrimethamine, trimethoprim, ondansetron, rabeprazole, and verapamil. Simulations showed that co-administration of perpetrators increased plasma exposures to metformin, which were consistent with clinic observations. Sensitivity analysis demonstrated that contributions of the tested factors to metformin DDI with cimetidine are gastrointestinal transit rate > inhibition of renal OCT2 ≈ inhibition of renal MATEs > inhibition of intestinal OCT3 > intestinal pH > inhibition of hepatic OCT1. Individual contributions of transporters to metformin disposition are renal OCT2 ≈ renal MATEs > intestinal OCT3 > hepatic OCT1 > intestinal PMAT. In conclusion, DDIs of metformin with perpetrators are attributed to integrated effects of inhibitions of these transporters.

Physiologically-Based Pharmacokinetic Modelling of Creatinine-Drug Interactions in the Chronic Kidney Disease Population

Elevated serum creatinine (S_Cr ) caused by the inhibition of renal transporter(s) may be misinterpreted as kidney injury. The interpretation is more complicated in patients with chronic kidney disease (CKD) due to altered disposition of creatinine and renal transporter inhibitors. A clinical study was conducted in 17 patients with CKD (estimated glomerular filtration rate 15-59 mL/min/1.73 m² ); changes in S_Cr were monitored during trimethoprim treatment (100-200 mg/day), administered to prevent recurrent urinary infection, relative to the baseline level. Additional S_Cr -interaction data with trimethoprim, cimetidine, and famotidine in patients with CKD were collated from the literature. Our published physiologically-based creatinine model was extended to predict the effect of the CKD on S_Cr and creatinine-drug interaction. The creatinine-CKD model incorporated age/sex-related differences in creatinine synthesis, CKD-related glomerular filtration deterioration; change in transporter activity either proportional or disproportional to glomerular filtration rate (GFR) decline were explored. Optimized models successfully recovered baseline S_Cr from 64 patients with CKD (geometric mean fold-error of 1.1). Combined with pharmacokinetic models of inhibitors, the creatinine model was used to simulate transporter-mediated creatinine-drug interactions. Use of inhibitor unbound plasma concentrations resulted in 66% of simulated S_Cr interaction data within the prediction limits, with cimetidine interaction significantly underestimated. Assuming that transporter activity deteriorates disproportional to GFR decline resulted in higher predicted sensitivity to transporter inhibition in patients with CKD relative to healthy patients, consistent with sparse clinical data. For the first time, this novel modelling approach enables quantitative prediction of S_Cr in CKD and delineation of the effect of disease and renal transporter inhibition in this patient population.

A Physiologically-Based Pharmacokinetic Model of Trimethoprim for MATE1, OCT1, OCT2, and CYP2C8 Drug-Drug-Gene Interaction Predictions

Trimethoprim is a frequently-prescribed antibiotic and therefore likely to be co-administered with other medications, but it is also a potent inhibitor of multidrug and toxin extrusion protein (MATE) and a weak inhibitor of cytochrome P450 (CYP) 2C8. The aim of this work was to develop a physiologically-based pharmacokinetic (PBPK) model of trimethoprim to investigate and predict its drug–drug interactions (DDIs). The model was developed in PK-Sim^®, using a large number of clinical studies (66 plasma concentration–time profiles with 36 corresponding fractions excreted in urine) to describe the trimethoprim pharmacokinetics over the entire published dosing range (40 to 960 mg). The key features of the model include intestinal efflux via P-glycoprotein (P-gp), metabolism by CYP3A4, an unspecific hepatic clearance process, and a renal clearance consisting of glomerular filtration and tubular secretion. The DDI performance of this new model was demonstrated by prediction of DDIs and drug–drug–gene interactions (DDGIs) of trimethoprim with metformin, repaglinide, pioglitazone, and rifampicin, with all predicted DDI and DDGI AUC_last and C_max ratios within 1.5-fold of the clinically-observed values. The model will be freely available in the Open Systems Pharmacology model repository, to support DDI studies during drug development.

Identification of Appropriate Endogenous Biomarker for Risk Assessment of Multidrug and Toxin Extrusion Protein-Mediated Drug-Drug Interactions in Healthy Volunteers

Endogenous biomarkers are emerging to advance clinical drug‐drug interaction (DDI) risk assessment in drug development. Twelve healthy subjects received a multidrug and toxin exclusion protein (MATE) inhibitor (pyrimethamine, 10, 25, and 75 mg) in a crossover fashion to identify an appropriate endogenous biomarker to assess MATE1/2‐K‐mediated DDI in the kidneys. Metformin (500 mg) was also given as reference probe drug for MATE1/2‐K. In addition to the previously reported endogenous biomarker candidates (creatinine and N¹‐methylnicotinamide (1‐NMN)), N¹‐methyladenosine (m¹A) was included as novel biomarkers. 1‐NMN and m¹A presented as superior MATE1/2‐K biomarkers since changes in their renal clearance (CL_r) along with pyrimethamine dose were well‐correlated with metformin CL_r changes. The CL_r of creatinine was reduced by pyrimethamine, however, its changes poorly correlated with metformin CL_r changes. Nonlinear regression analysis (CL_r vs. mean total concentration of pyrimethamine in plasma) yielded an estimate of the inhibition constant (K_i) of pyrimethamine and the fraction of the clearance pathway sensitive to pyrimethamine. The in vivoK_i value thus obtained was further converted to unbound Ki using plasma unbound fraction of pyrimethamine, which was comparable to the in vitroK_i for MATE1 (1‐NMN) and MATE2‐K (1‐NMN and m¹A). It is concluded that 1‐NMN and m¹A CL_r can be leveraged as quantitative MATE1/2‐K biomarkers for DDI risk assessment in healthy volunteers.

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

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

A 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.

Mechanistic Models as Framework for Understanding Biomarker Disposition: Prediction of Creatinine-Drug Interactions

Creatinine is widely used as a biomarker of glomerular filtration, and, hence, renal function. However, transporter-mediated secretion also contributes to its renal clearance, albeit to a lesser degree. Inhibition of these transporters causes transient serum creatinine elevation, which can be mistaken as impaired renal function. The current study developed mechanistic models of creatinine kinetics within physiologically based framework accounting for multiple transporters involved in creatinine renal elimination, assuming either unidirectional or bidirectional-OCT2 transport (driven by electrochemical gradient). Robustness of creatinine models was assessed by predicting creatinine-drug interactions with 10 perpetrators; performance evaluation accounted for 5% intra-individual variability in serum creatinine. Models showed comparable predictive performances of the maximum steady-state effect regardless of OCT2 directionality assumptions. However, only the bidirectional-OCT2 model successfully predicted the minimal effect of ranitidine. The dynamic nature of models provides clear advantage to static approaches and most advanced framework for evaluating interplay between multiple processes in creatinine renal disposition.

Phase 1 study of veliparib (ABT-888), a poly (ADP-ribose) polymerase inhibitor, with carboplatin and paclitaxel in advanced solid malignancies

PURPOSE

Veliparib is an oral inhibitor of poly (ADP-ribose) polymerase (PARP)-1 and -2. PARP-1 expression may be increased in cancer, and this increase confers resistance to cytotoxic agents. We aimed to determine the recommended phase 2 dose (RP2D), maximum tolerated dose (MTD), dose-limiting toxicity (DLT), and pharmacokinetics (PK) of veliparib combined with paclitaxel and carboplatin.

METHODS

Eligibility criteria included patients with advanced solid tumors treated with ≤ 3 prior regimens. Paclitaxel and carboplatin were administered on day 3 of a 21-day cycle. Veliparib was given PO BID days 1-7, except for cycle 1 in the first 46 patients to serve as control for toxicity and PK. A standard "3 + 3" design started veliparib at 10 mg BID, paclitaxel at 150 mg/m², and carboplatin AUC 6. The pharmacokinetic (PK) disposition of veliparib, paclitaxel, and carboplatin was determined by LC-MS/MS and AAS during cycles 1 and 2.

RESULTS

Seventy-three patients were enrolled. Toxicities were as expected with carboplatin/paclitaxel chemotherapy, including neutropenia, thrombocytopenia, and peripheral neuropathy. DLTs were seen in two of seven evaluable patients at the maximum administered dose (MAD): veliparib 120 mg BID, paclitaxel 200 mg/m², and carboplatin AUC 6 (febrile neutropenia, hyponatremia). The MTD and RP2D were determined to be veliparib 100 mg BID, paclitaxel 200 mg/m², and carboplatin AUC 6. Median number of cycles of the three-agent combination was 4 (1-16). We observed 22 partial and 5 complete responses. Veliparib did not affect paclitaxel or carboplatin PK disposition.

CONCLUSION

Veliparib, paclitaxel, and carboplatin were well tolerated and demonstrated promising antitumor activity.

Estimation of changes in serum creatinine and creatinine clearance caused by renal transporter inhibition in healthy subjects

Creatinine is excreted into urine by glomerular filtration and renal tubular secretion through drug transporters such as organic anion transporter 2 (OAT2), organic cation transporter 2 (OCT2), OCT3, multidrug and toxin extrusion protein 1 (MATE1), and MATE2-K. We aimed to investigate whether our method for estimating percentage changes in serum creatinine concentration (SCr) and creatinine clearance (CL_cre) from the baseline is applicable for studying renal transporter inhibitors. We tested 14 compounds (cimetidine, cobicistat, dolutegravir, dronedarone, DX-619, famotidine, INCB039110, nizatidine, ondansetron, pyrimethamine, rabeprazole, ranolazine, trimethoprim, and vandetanib), which were reported to cause reversible changes in SCr and/or CL_cre in healthy subjects excluding elderly. Percentage changes were estimated from the relative contributions of the forementioned transporters to CL_cre and competitive inhibition by these compounds at their maximum plasma unbound concentrations. For 7 and 9 out of these compounds, changes in SCr and/or CL_cre were estimated within 2- and 3-fold of observed values, respectively. Less than 10% changes in SCr and/or CL_cre caused by cobicistat, dolutegravir, and rabeprazole were reproduced as such by our method. These findings suggest that our method can be used to estimate changes in SCr and CL_cre caused by competitive inhibitions of renal drug transporters.