Efficacy and safety of zibotentan and dapagliflozin in patients with chronic kidney disease: study design and baseline characteristics of the ZENITH-CKD trial.

BACKGROUND: Sodium-glucose co-transporter 2 inhibitors (SGLT2is) are part of the standard of care for patients with chronic kidney disease (CKD), both with and without type 2 diabetes. Endothelin A (ETA) receptor antagonists have also been shown to slow progression of CKD. Differing mechanisms of action of SGLT2 and ETA receptor antagonists may enhance efficacy. We outline a study to evaluate the effect of combination zibotentan/dapagliflozin versus dapagliflozin alone on albuminuria and estimated glomerular filtration rate (eGFR). METHODS: We are conducting a double-blind, active-controlled, Phase 2b study to evaluate the efficacy and safety of ETA receptor antagonist zibotentan and SGLT2i dapagliflozin in a planned 415 adults with CKD (Zibotentan and Dapagliflozin for the Treatment of CKD; ZENITH-CKD). Participants are being randomized (1:2:2) to zibotentan 0.25 mg/dapagliflozin 10 mg once daily (QD), zibotentan 1.5 mg/dapagliflozin 10 mg QD and dapagliflozin 10 mg QD alone, for 12 weeks followed by a 2-week off-treatment wash-out period. The primary endpoint is the change in log-transformed urinary albumin-to-creatinine ratio (UACR) from baseline to Week 12. Other outcomes include change in blood pressure from baseline to Week 12 and change in eGFR the study. The incidence of adverse events will be monitored. Study protocol-defined events of special interest include changes in fluid-related measures (weight gain or B-type natriuretic peptide). RESULTS: A total of 447 patients were randomized and received treatment in placebo/dapagliflozin (n = 177), zibotentan 0.25 mg/dapagliflozin (n = 91) and zibotentan 1.5 mg/dapagliflozin (n =  179). The mean age was 62.8 years, 30.9% were female and 68.2% were white. At baseline, the mean eGFR of the enrolled population was 46.7 mL/min/1.73 m2 and the geometric mean UACR was 538.3 mg/g. CONCLUSION: This study evaluates the UACR-lowering efficacy and safety of zibotentan with dapagliflozin as a potential new treatment for CKD. The study will provide information about an effective and safe zibotentan dose to be further investigated in a Phase 3 clinical outcome trial. CLINICAL TRIAL REGISTRATION NUMBER: NCT04724837.

The sodium glucose co-transporter 2 inhibitor dapagliflozin ameliorates the fluid-retaining effect of the endothelin A receptor antagonist zibotentan.

BACKGROUND: Endothelin A receptor antagonists (ETARA) slow chronic kidney disease (CKD) progression but their use is limited due to fluid retention and associated clinical risks. Sodium-glucose co-transporter 2 inhibitors (SGLT2i) cause osmotic diuresis and improve clinical outcomes in CKD and heart failure. We hypothesized that co-administration of the SGLT2i dapagliflozin with the ETARA zibotentan would mitigate the fluid retention risk using hematocrit (Hct) and bodyweight as proxies for fluid retention. METHODS: Experiments were performed in 4% salt fed WKY rats. First, we determined the effect of zibotentan (30, 100 or 300 mg/kg/day) on Hct and bodyweight. Second, we assessed the effect of zibotentan (30 or 100 mg/kg/day) alone or in combination with dapagliflozin (3 mg/kg/day) on Hct and bodyweight. RESULTS: Hct at Day 7 was lower in zibotentan versus vehicle groups [zibotentan 30 mg/kg/day, 43% (standard error 1); 100 mg/kg/day, 42% (1); and 300 mg/kg/day, 42% (1); vs vehicle, 46% (1); P < .05], while bodyweight was numerically higher in all zibotentan groups compared with vehicle. Combining zibotentan with dapagliflozin for 7 days prevented the change in Hct [zibotentan 100 mg/kg/day and dapagliflozin, 45% (1); vs vehicle 46% (1); P = .44] and prevented the zibotentan-driven increase in bodyweight (zibotentan 100 mg/kg/day + dapagliflozin 3 mg/kg/day = -3.65 g baseline corrected bodyweight change; P = .15). CONCLUSIONS: Combining ETARA with SGLT2i prevents ETARA-induced fluid retention, supporting clinical studies to assess the efficacy and safety of combining zibotentan and dapagliflozin in individuals with CKD.

Physiologically based pharmacokinetics modeling and transporter proteomics to predict systemic and local liver and muscle disposition of statins.

Statins are used to reduce liver cholesterol levels but also carry a dose-related risk of skeletal muscle toxicity. Concentrations of statins in plasma are often used to assess efficacy and safety, but because statins are substrates of membrane transporters that are present in diverse tissues, local differences in intracellular tissue concentrations cannot be ruled out. Thus, plasma concentration may not be an adequate indicator of efficacy and toxicity. To bridge this gap, we used physiologically based pharmacokinetic (PBPK) modeling to predict intracellular concentrations of statins. Quantitative data on transporter clearance were scaled from in vitro to in vivo conditions by integrating targeted proteomics and transporter kinetics data. The developed PBPK models, informed by proteomics, suggested that organic anion-transporting polypeptide 2B1 (OATP2B1) and multidrug resistance-associated protein 1 (MRP1) play a pivotal role in the distribution of statins in muscle. Using these PBPK models, we were able to predict the impact of alterations in transporter function due to genotype or drug-drug interactions on statin systemic concentrations and exposure in liver and muscle. These results underscore the potential of proteomics-guided PBPK modeling to scale transporter clearance from in vitro data to real-world implications. It is important to evaluate the role of drug transporters when predicting tissue exposure associated with on- and off-target effects.

Feedback modeling of non-esterified fatty acids in obese Zucker rats after nicotinic acid infusions.

This study investigates the impact of disease on nicotinic acid (NiAc)-induced changes in plasma concentrations of non-esterified fatty acids (NEFA). NiAc was given by constant intravenous infusion to normal Sprague-Dawley and obese Zucker rats, and arterial blood samples were taken for analysis of NiAc, NEFA, insulin and glucose plasma concentrations. The intravenous route was intentionally selected to avoid confounding processes, such as absorption, following extravascular administration. Data were analyzed using nonlinear mixed effects modeling (NONMEM, version VI). The disposition of NiAc in the normal rats was described by a two-compartment model with endogenous synthesis of NiAc and two parallel capacity-limited elimination processes. In the obese rats disposition was described by a one-compartment model with endogenous synthesis of NiAc and one capacity-limited elimination process. The plasma concentration of NiAc drove NEFA (R) turnover via an inhibitory drug-mechanism function acting on the formation of NEFA. NEFA turnover was described by a feedback model with a moderator distributed over a series of transit compartments, where the first compartment (M 1 ) inhibited the formation of R and the last compartment (M N ) stimulated the loss of R. All processes regulating plasma NEFA concentrations were assumed to be captured by the moderator function. Differences in the pharmacodynamic response of the two strains included, in the obese animals, an increased NEFA baseline, diminished rebound and post-rebound oscillation, and a more pronounced slowly developing tolerance during the period of constant drug exposure. The feedback model captured the NiAc-induced changes in NEFA response in both the normal and obese rats. Differences in the parameter estimates between the obese and normal rats included, in the former group, increases in R 0 , k in and p by 44, 41 and 78 %, respectively, and decreases in k out and γ by 64 and 84 %, respectively. The estimates of k tol and IC 50 were similar in both groups. The NiAc-NEFA concentration-response relationship at equilibrium was substantially different in the two groups, being shifted upwards and to the right, and being shallower in the obese rats. The extent of such shifts is important, as they demonstrate the impact of disease at equilibrium and, if ignored, will lead to erroneous dose predictions and, in consequence, poorly designed studies. The proposed models are primarily aimed at screening and selecting candidates with the highest potential of becoming a viable drug in man.

Challenges of a mechanistic feedback model describing nicotinic acid-induced changes in non-esterified fatty acids in rats.

Previously, we developed a feedback model to describe the tolerance and oscillatory rebound of non-esterified fatty acid (NEFA) plasma concentrations in male Sprague Dawley rats after intravenous infusions of nicotinic acid (NiAc). This study challenges that model, using the following regimens of intravenous and oral NiAc dosing in male Sprague Dawley rats (n = 95) to create different patterns of exposure: (A) 30 min infusion at 0, 1, 5 or 20 µmol kg(-1) body weight; (B) 300 min infusion at 0, 5, 10 or 51 µmol kg(-1); (C) 30 min infusion at 5 µmol kg(-1), followed by a stepwise decrease in rate every 10 min for 180 min; (D) 30 min infusion at 5 µmol kg(-1), followed by a stepwise decrease in rate every 10 min for 180 min and another 30 min infusion at 5 µmol kg(-1) from 210 to 240 min; (E) an oral dose of 0, 24.4, 81.2 or 812 µmol kg(-1). Serial arterial blood samples were taken for measurement of plasma NiAc and NEFA concentrations. The gradual decrease in infusion rate in (C) and (D) were also designed to test the hypothesis that a gradual reduction in NiAc plasma concentration may be expected to reduce or prevent rebound. The absorption of NiAc was described by parallel linear and non-linear processes and the disposition of NiAc by a two-compartment model with endogenous turnover rate and two parallel capacity-limited elimination processes. NEFA (R) turnover, which was driven by the plasma concentration of NiAc via an inhibitory drug-mechanism function acting on NEFA formation, was described by a feedback model with a moderator distributed over a series of transit compartments, where the first compartment (M 1) inhibited the formation of R and the last compartment (M N ) stimulated the loss of R. All processes regulating the plasma NEFA concentration were assumed to be captured by the moderator function. Data were analyzed using non-linear mixed effects modeling (NONMEM). The potency IC 50 of NiAc was 68 nmol L(-1), the fractional turnover rate k out 0.27 L mmol(-1) min(-1), and the turnover rate of moderator k tol 0.023 min(-1). The lower physiological limit of NEFA, which was modeled as a NiAc-independent release (k cap ) of NEFA into plasma, was estimated to 0.023 mmol L(-1) min(-1). The parameter estimates derived in this study were consistent with our previous estimates, suggesting that the model may be used for prediction of the NEFA response time-course following different modes and routes administration of NiAc or NiAc analogues. In order to avoid NiAc-induced NEFA rebound, a slow decline in the NiAc exposure pattern is needed at or below IC (50).

Understanding Statin-Roxadustat Drug-Drug-Disease Interaction Using Physiologically-Based Pharmacokinetic Modeling.

A different drug-drug interaction (DDI) scenario may exist in patients with chronic kidney disease (CKD) compared with healthy volunteers (HVs), depending on the interplay between drug-drug and disease (drug-drug-disease interaction (DDDI)). Physiologically-based pharmacokinetic (PBPK) modeling, in lieu of a clinical trial, is a promising tool for evaluating these complex DDDIs in patients. However, the prediction confidence of PBPK modeling in the severe CKD population is still low when nonrenal pathways are involved. More mechanistic virtual disease population and robust validation cases are needed. To this end, we aimed to: (i) understand the implications of severe CKD on statins (atorvastatin, simvastatin, and rosuvastatin) pharmacokinetics (PK) and DDI; and (ii) predict untested clinical scenarios of statin-roxadustat DDI risks in patients to guide suitable dose regimens. A novel virtual severe CKD population was developed incorporating the disease effect on both renal and nonrenal pathways. Drug and disease PBPK models underwent a four-way validation. The verified PBPK models successfully predicted the altered PKs in patients for substrates and inhibitors and recovered the observed statin-rifampicin DDIs in patients and the statin-roxadustat DDIs in HVs within 1.25- and 2-fold error. Further sensitivity analysis revealed that the severe CKD effect on statins PK is mainly mediated by hepatic BCRP for rosuvastatin and OATP1B1/3 for atorvastatin. The magnitude of statin-roxadustat DDI in patients with severe CKD was predicted to be similar to that in HVs. PBPK-guided suitable dose regimens were identified to minimize the risk of side effects or therapeutic failure of statins when co-administered with roxadustat.

Does the choice of applied physiologically-based pharmacokinetics platform matter? A case study on simvastatin disposition and drug-drug interaction.

Physiologically-based pharmacokinetic (PBPK) models have an important role in drug discovery/development and decision making in regulatory submissions. This is facilitated by predefined PBPK platforms with user-friendly graphical interface, such as Simcyp and PK-Sim. However, evaluations of platform differences and the potential implications for disposition-related applications are still lacking. The aim of this study was to assess how PBPK model development, input parameters, and model output are affected by the selection of PBPK platform. This is exemplified via the establishment of simvastatin PBPK models (workflow, final models, and output) in PK-Sim and Simcyp as representatives of established whole-body PBPK platforms. The major finding was that the choice of PBPK platform influenced the model development strategy and the final model input parameters, however, the predictive performance of the simvastatin models was still comparable between the platforms. The main differences between the structure and implementation of Simcyp and PK-Sim were found in the absorption and distribution models. Both platforms predicted equally well the observed simvastatin (lactone and acid) pharmacokinetics (20-80 mg), BCRP and OATP1B1 drug-gene interactions (DGIs), and drug-drug interactions (DDIs) when co-administered with CYP3A4 and OATP1B1 inhibitors/inducers. This study illustrates that in-depth knowledge of established PBPK platforms is needed to enable an assessment of the consequences of PBPK platform selection. Specifically, this work provides insights on software differences and potential implications when bridging PBPK knowledge between Simcyp and PK-Sim users. Finally, it provides a simvastatin model implemented in both platforms for risk assessment of metabolism- and transporter-mediated DGIs and DDIs.

Feedback modeling of non-esterified fatty acids in rats after nicotinic acid infusions.

A feedback model was developed to describe the tolerance and oscillatory rebound seen in non-esterified fatty acid (NEFA) plasma concentrations following intravenous infusions of nicotinic acid (NiAc) to male Sprague-Dawley rats. NiAc was administered as an intravenous infusion over 30 min (0, 1, 5 or 20 µmol kg(-1) of body weight) or over 300 min (0, 5, 10 or 51 µmol kg(-1) of body weight), to healthy rats (n = 63), and serial arterial blood samples were taken for measurement of NiAc and NEFA plasma concentrations. Data were analyzed using nonlinear mixed effects modeling (NONMEM). The disposition of NiAc was described by a two-compartment model with endogenous turnover rate and two parallel capacity-limited elimination processes. The plasma concentration of NiAc was driving NEFA (R) turnover via an inhibitory drug-mechanism function acting on the formation of NEFA. The NEFA turnover was described by a feedback model with a moderator distributed over a series of transit compartments, where the first compartment (M (1)) inhibited the formation of R and the last compartment (M ( N )) stimulated the loss of R. All processes regulating plasma NEFA concentrations were assumed to be captured by the moderator function. The potency, IC (50), of NiAc was 45 nmol L(-1), the fractional turnover rate k ( out ) was 0.41 L mmol(-1) min(-1) and the turnover rate of moderator k ( tol ) was 0.027 min(-1). A lower physiological limit of NEFA was modeled as a NiAc-independent release (k ( cap )) of NEFA into plasma and was estimated to 0.032 mmol L(-1) min(-1). This model can be used to provide information about factors that determine the time-course of NEFA response following different modes, rates and routes of administration of NiAc. The proposed model may also serve as a preclinical tool for analyzing and simulating drug-induced changes in plasma NEFA concentrations after treatment with NiAc or NiAc analogues.

ADME: Assessing Pharmacokinetic-Pharmacodynamic Parameters of Oligonucleotides.

We describe tactics to assess pharmacokinetic (PK) and pharmacodynamic (PD) parameters of oligonucleotides. The chapter includes recommendations on the design of single-dose preclinical PK studies, preclinical PKPD studies, and toxicological studies, and on best practice for scaling PK and PD parameters from animal to human. We focus on single-stranded oligonucleotides, but relevant differences to double-stranded RNAs are also addressed.

Erratum: Publisher Correction: Mathematical model of hemodynamic mechanisms and consequences of glomerular hypertension in diabetic mice.

[This corrects the article DOI: 10.1038/s41540-018-0077-9.].

Mathematical model of hemodynamic mechanisms and consequences of glomerular hypertension in diabetic mice.

Many preclinically promising therapies for diabetic kidney disease fail to provide efficacy in humans, reflecting limited quantitative translational understanding between rodent models and human disease. To quantitatively bridge interspecies differences, we adapted a mathematical model of renal function from human to mice, and incorporated adaptive and pathological mechanisms of diabetes and nephrectomy to describe experimentally observed changes in glomerular filtration rate (GFR) and proteinuria in db/db and db/db UNX (uninephrectomy) mouse models. Changing a small number of parameters, the model reproduced interspecies differences in renal function. Accounting for glucose and Na+ reabsorption through sodium glucose cotransporter 2 (SGLT2), increasing blood glucose and Na+ intake from normal to db/db levels mathematically reproduced glomerular hyperfiltration observed experimentally in db/db mice. This resulted from increased proximal tubule sodium reabsorption, which elevated glomerular capillary hydrostatic pressure (P gc) in order to restore sodium balance through increased GFR. Incorporating adaptive and injurious effects of elevated P gc, we showed that preglomerular arteriole hypertrophy allowed more direct transmission of pressure to the glomerulus with a smaller mean arterial pressure rise; Glomerular hypertrophy allowed a higher GFR for a given P gc; and P gc-driven glomerulosclerosis and nephron loss reduced GFR over time, while further increasing P gc and causing moderate proteinuria, in agreement with experimental data. UNX imposed on diabetes increased P gc further, causing faster GFR decline and extensive proteinuria, also in agreement with experimental data. The model provides a mechanistic explanation for hyperfiltration and proteinuria progression that will facilitate translation of efficacy for novel therapies from mouse models to human.

Antisense oligonucleotide treatment ameliorates IFN-γ-induced proteinuria in APOL1-transgenic mice.

African Americans develop end-stage renal disease at a higher rate compared with European Americans due to 2 polymorphisms (G1 and G2 risk variants) in the apolipoprotein L1 (APOL1) gene common in people of African ancestry. Although this compelling genetic evidence provides an exciting opportunity for personalized medicine in chronic kidney disease, drug discovery efforts have been greatly hindered by the fact that APOL1 expression is lacking in rodents. Here, we describe a potentially novel physiologically relevant genomic mouse model of APOL1-associated renal disease that expresses human APOL1 from the endogenous human promoter, resulting in expression in similar tissues and at similar relative levels as humans. While naive APOL1-transgenic mice did not exhibit a renal disease phenotype, administration of IFN-γ was sufficient to robustly induce proteinuria only in APOL1 G1 mice, despite inducing kidney APOL1 expression in both G0 and G1 mice, serving as a clinically relevant "second hit." Treatment of APOL1 G1 mice with IONIS-APOL1Rx, an antisense oligonucleotide (ASO) targeting APOL1 mRNA, prior to IFN-γ challenge robustly and dose-dependently inhibited kidney and liver APOL1 expression and protected against IFN-γ-induced proteinuria, indicating that the disease-relevant cell types are sensitive to ASO treatment. Therefore, IONIS-APOL1Rx may be an effective therapeutic for APOL1 nephropathies and warrants further development.

Overexpressing cell systems are a competitive option to primary adipocytes when predicting in vivo potency of dual GPR81/GPR109A agonists.

Mathematical models predicting in vivo pharmacodynamic effects from in vitro data can accelerate drug discovery, and reduce costs and animal use. However, data integration and modeling is non-trivial when more than one drug-target receptor is involved in the biological response. We modeled the inhibition of non-esterified fatty acid release by dual G-protein-coupled receptor 81/109A (GPR81/GPR109A) agonists in vivo in the rat, to estimate the in vivo EC50 values for 12 different compounds. We subsequently predicted those potency estimates using EC50 values obtained from concentration-response data in isolated primary adipocytes and cell systems overexpressing GPR81 or GPR109A in vitro. A simple linear regression model based on data from primary adipocytes predicted the in vivo EC50 better than simple linear regression models based on in vitro data from either of the cell systems. Three models combining the data from the overexpressing cell systems were also evaluated: two piecewise linear models defining logical OR- and AND-circuits, and a multivariate linear regression model. All three models performed better than the simple linear regression model based on data from primary adipocytes. The OR-model was favored since it is likely that activation of either GPR81 or GPR109A is sufficient to deactivate the cAMP pathway, and thereby inhibit non-esterified fatty acid release. The OR-model was also able to predict the in vivo selectivity between the two receptors. Finally, the OR-model was used to predict the in vivo potency of 1651 new compounds. This work suggests that data from the overexpressing cell systems are sufficient to predict in vivo potency of GPR81/GPR109A agonists, an approach contributing to faster and leaner drug discovery.

Mixed Effects Modeling Using Stochastic Differential Equations: Illustrated by Pharmacokinetic Data of Nicotinic Acid in Obese Zucker Rats.

Inclusion of stochastic differential equations in mixed effects models provides means to quantify and distinguish three sources of variability in data. In addition to the two commonly encountered sources, measurement error and interindividual variability, we also consider uncertainty in the dynamical model itself. To this end, we extend the ordinary differential equation setting used in nonlinear mixed effects models to include stochastic differential equations. The approximate population likelihood is derived using the first-order conditional estimation with interaction method and extended Kalman filtering. To illustrate the application of the stochastic differential mixed effects model, two pharmacokinetic models are considered. First, we use a stochastic one-compartmental model with first-order input and nonlinear elimination to generate synthetic data in a simulated study. We show that by using the proposed method, the three sources of variability can be successfully separated. If the stochastic part is neglected, the parameter estimates become biased, and the measurement error variance is significantly overestimated. Second, we consider an extension to a stochastic pharmacokinetic model in a preclinical study of nicotinic acid kinetics in obese Zucker rats. The parameter estimates are compared between a deterministic and a stochastic NiAc disposition model, respectively. Discrepancies between model predictions and observations, previously described as measurement noise only, are now separated into a comparatively lower level of measurement noise and a significant uncertainty in model dynamics. These examples demonstrate that stochastic differential mixed effects models are useful tools for identifying incomplete or inaccurate model dynamics and for reducing potential bias in parameter estimates due to such model deficiencies.

Joint feedback analysis modeling of nonesterified fatty acids in obese Zucker rats and normal Sprague-Dawley rats after different routes of administration of nicotinic acid.

Data were pooled from several studies on nicotinic acid (NiAc) intervention of fatty acid turnover in normal Sprague-Dawley and obese Zucker rats in order to perform a joint PKPD of data from more than 100 normal Sprague-Dawley and obese Zucker rats, exposed to several administration routes and rates. To describe the difference in pharmacodynamic parameters between obese and normal rats, we modified a previously published nonlinear mixed effects model describing tolerance and oscillatory rebound effects of NiAc on nonesterified fatty acids plasma concentrations. An important conclusion is that planning of experiments and dose scheduling cannot rely on pilot studies on normal animals alone. The obese rats have a less-pronounced concentration-response relationship and need higher doses to exhibit desired response. The relative level of fatty acid rebound after cessation of NiAc administration was also quantified in the two rat populations. Building joint normal-disease models with scaling parameter(s) to characterize the "degree of disease" can be a useful tool when designing informative experiments on diseased animals, particularly in the preclinical screen. Data were analyzed using nonlinear mixed effects modeling, for the optimization, we used an improved method for calculating the gradient than the usually adopted finite difference approximation.

Quantitative analysis of rate and extent of tolerance of biomarkers: application to nicotinic acid-induced changes in non-esterified fatty acids in rats.

In this paper we quantitatively evaluate two feedback systems with a focus on rate and extent of tolerance and rebound development. In the two feedback systems, the regulation of turnover of response is governed by one or several moderators. In the basic system, one single moderator inhibits the formation of response. This system has been applied to cortisol secretion and serotonin reuptake inhibition. The basic system has been extended to adequately describe nicotinic acid (NiAc)-induced changes in non-esterified fatty acids (NEFA). In the extended system, the feedback is described by a cascade of moderators where the first inhibits formation of response and the last stimulates loss of response. The objectives of this paper were to analyze these systems from a mathematical/analytical and quantitative point of view and to present simulations with different parameter settings and dosing regimens in order to highlight the intrinsic behaviour of these systems and to present expressions and graphs that are applicable for quantification of rate and extent of tolerance and rebound. The dynamics of the moderators (k(tol)) compared to the dynamics of the response (k(out)), was shown to be important for the behaviour of both systems. For instance, slow dynamics of the moderator compared to the response (k(tol)<