Mechanistic modelling of tesaglitazar pharmacokinetic data in subjects with various degrees of renal function--evidence of interconversion

In vivo liposomal delivery of PPARα/γ dual agonist tesaglitazar in a model of obesity enriches macrophage targeting and limits liver and kidney drug effects

Macrophages are important regulators of obesity-associated inflammation and PPARα and -γ agonism in macrophages has anti-inflammatory effects. In this study, we tested the efficacy with which liposomal delivery could target the PPARα/γ dual agonist tesaglitazar to macrophages while reducing drug action in common sites of drug toxicity: the liver and kidney, and whether tesaglitazar had anti-inflammatory effects in an in vivo model of obesity-associated dysmetabolism. Methods: Male leptin-deficient (ob/ob) mice were administered tesaglitazar or vehicle for one week in a standard oral formulation or encapsulated in liposomes. Following the end of treatment, circulating metabolic parameters were measured and pro-inflammatory adipose tissue macrophage populations were quantified by flow cytometry. Cellular uptake of liposomes in tissues was assessed using immunofluorescence and a broad panel of cell subset markers by flow cytometry. Finally, PPARα/γ gene target expression levels in the liver, kidney, and sorted macrophages were quantified to determine levels of drug targeting to and drug action in these tissues and cells. Results: Administration of a standard oral formulation of tesaglitazar effectively treated symptoms of obesity-associated dysmetabolism and reduced the number of pro-inflammatory adipose tissue macrophages. Macrophages are the major cell type that took up liposomes with many other immune and stromal cell types taking up liposomes to a lesser extent. Liposome delivery of tesaglitazar did not have effects on inflammatory macrophages nor did it improve metabolic parameters to the extent of a standard oral formulation. Liposomal delivery did, however, attenuate effects on liver weight and liver and kidney expression of PPARα and -γ gene targets compared to oral delivery. Conclusions: These findings reveal for the first time that tesaglitazar has anti-inflammatory effects on adipose tissue macrophage populations in vivo. These data also suggest that while nanoparticle delivery reduced off-target effects, yet the lack of tesaglitazar actions in non-targeted cells such (as hepatocytes and adipocytes) and the uptake of drug-loaded liposomes in many other cell types, albeit to a lesser extent, may have impacted overall therapeutic efficacy. This fulsome analysis of cellular uptake of tesaglitazar-loaded liposomes provides important lessons for future studies of liposome drug delivery.

PPAR agonists reduce steatosis in oleic acid-overloaded HepaRG cells

Although non-alcoholic fatty liver disease (NAFLD) is currently the most common form of chronic liver disease there is no pharmacological agent approved for its treatment. Since peroxisome proliferator-activated receptors (PPARs) are closely associated with hepatic lipid metabolism, they seem to play important roles in NAFLD. However, the effects of PPAR agonists on steatosis that is a common pathology associated with NAFLD, remain largely controversial. In this study, the effects of various PPAR agonists, i.e. fenofibrate, bezafibrate, troglitazone, rosiglitazone, muraglitazar and tesaglitazar on oleic acid-induced steatotic HepaRG cells were investigated after a single 24-hour or 2-week repeat treatment. Lipid vesicles stained by Oil-Red O and triglycerides accumulation caused by oleic acid overload, were decreased, by up to 50%, while fatty acid oxidation was induced after 2-week co-treatment with PPAR agonists. The greatest effects on reduction of steatosis were obtained with the dual PPARα/γ agonist muraglitazar. Such improvement of steatosis was associated with up-regulation of genes related to fatty acid oxidation activity and down-regulation of many genes involved in lipogenesis. Moreover, modulation of expression of some nuclear receptor genes, such as FXR, LXRα and CAR, which are potent actors in the control of lipogenesis, was observed and might explain repression of de novo lipogenesis.

CONCLUSION

Altogether, our in vitro data on steatotic HepaRG cells treated with PPAR agonists correlated well with clinical investigations, bringing a proof of concept that drug-induced reversal of steatosis in human can be evaluated in in vitro before conducting long-term and costly in vivo studies in animals and patients.

A Nonlinear Mixed Effects Pharmacokinetic Model for Dapagliflozin and Dapagliflozin 3-O-glucuronide in Renal or Hepatic Impairment

Dapagliflozin is a sodium–glucose co-transporter 2 inhibitor in development for the treatment of type 2 diabetes mellitus. A semi-mechanistic population pharmacokinetic (PK) model was developed for dapagliflozin and its inactive metabolite dapagliflozin 3-O-glucuronide (D3OG) with emphasis on renal and hepatic contribution to dapagliflozin metabolism. Renal and hepatic impairment decreased the clearance of dapagliflozin to D3OG and the clearance of D3OG. The fraction of D3OG formed via the renal route decreased from 40–55% in subjects with normal renal function (creatinine clearance (CLcr) > 80 ml/min) to 10% in subjects with severe renal insufficiency (CLcr = 13 ml/min). The model-based simulations suggested that the increase of systemic exposure (AUC_ss) of dapagliflozin and D3OG was less than twofold in subjects with mild or moderate renal impairment. This population modeling analysis presents a useful approach to evaluate the impact of renal and hepatic function on the PK of dapagliflozin.

Dual PPAR α / γ Agonism Normalizes Lipoprotein Profile of Renal Dyslipidemia

Chronic kidney disease (CKD) is characterised by specific lipoprotein abnormalities and insulin resistance. Dual activation of the peroxisome proliferators-activated receptors (PPAR) α and γ can significantly improve insulin sensitivity. The aim of the study was to investigate the effects of a dual PPAR α / γ agonist on lipoprotein abnormalities in patients with CKD. One mg of the dual PPAR α / γ agonist tesaglitazar was given once daily during six weeks to CKD patients, and to healthy subjects. Plasma lipids, apolipoproteins (apo) and discrete lipoprotein subclasses were measured at baseline and end of treatment. In the CKD patients apoA-I increased significantly by 9%, and apoB decreased by 18%. There was an increase of apoC-III in HDL by 30%, and a parallel decrease of apoC-III in VLDL + LDL by 13%. Both the apoB-containing cholesterol-rich and the triglyceride-rich subclasses decreased significantly. With the exception of ApoC-III,all plasma lipids apolipoproteins and lipoprotein subclasses were reduced by treatment down to similar levels as the baseline levels of a healthy group of reference subjects. This study suggests that by improving insulin sensitivity a dual PPAR α / γ agonist has the potential to normalise most of the lipoprotein abnormalities in patients with CKD.

Examining the safety of PPAR agonists - current trends and future prospects

INTRODUCTION

The peroxisome proliferator-activated receptor (PPAR)-α and -γ agonists, fibrates and glitazones, are effective treatments for dyslipidemia and type 2 diabetes mellitus, respectively, but exhibit class-related, as well as compound-specific safety characteristics.

AREAS COVERED

This article reviews the profiles of PPAR-α, PPAR-γ, and dual PPAR-α/γ agonists with regard to class-related and compound-specific efficacy and adverse effects. We explore how learnings from first-generation drugs are being applied to develop safer PPAR-targeted therapies.

EXPERT OPINION

The finding that rosiglitazone may increase risk for cardiovascular events has led to regulatory guidelines requiring demonstration of cardiovascular safety in appropriate outcome trials for new type 2 diabetes mellitus drugs. The emerging data on the possibly increased risk of bladder cancer with pioglitazone may prompt the need for post-approval safety studies for new drugs. Since PPAR-α and -γ affect key cardiometabolic risk factors (diabetic dyslipidemia, insulin resistance, hyperglycemia, and inflammation) in a complementary fashion, combining their benefits has emerged as a particularly attractive option. New PPAR-targeted therapies that balance the relative potency and/or activity toward PPAR-α and -γ have shown promise in retaining efficacy while reducing potential side effects.

Design, conduct, analysis, and interpretation of clinical studies in patients with impaired kidney function

Chronic kidney disease has been shown to alter the pharmacokinetics of drugs that are eliminated not only via the renal pathway but also by metabolism or nonrenal transport. Guidance documents from regulatory agencies on the pharmacokinetics of drugs in patients with impaired kidney function provide a framework for facilitating study design, conduct, data analysis, and the generation of dosing recommendations. Design considerations include establishment of appropriate enrollment criteria, selection of appropriate matched control group(s), and staging of impaired kidney function by estimated glomerular filtration rate or creatinine clearance. When studies in hemodialysis patients are conducted, optimizing the timing of characterization of the pharmacokinetics profile based on the schedule of hemodialysis sessions will allow for a robust assessment in these patients. In addition to traditional noncompartmental approaches, the use of pharmacometric approaches can integrate data from multiple clinical studies and provide a quantitative rationale for dose selection in patients with impaired kidney function. This article addresses the challenges and opportunities associated with the design, conduct, analysis, and interpretation of clinical studies to allow for their future facilitation and for the establishment of safe and efficacious dosing in patients with impaired kidney function.

Pharmacokinetic-pharmacodynamic assessment of the interrelationships between tesaglitazar exposure and renal function in patients with type 2 diabetes mellitus

The effects of tesaglitazar on renal function (assessed as urinary clearance of 125I-sodium iothalamate or estimated by the modification of diet in renal disease formula) were studied in a 24-week open-label trial in type 2 diabetes mellitus patients randomized to daily doses of either tesaglitazar 2 mg or pioglitazone 45 mg. The aim of the analysis was to develop a population pharmacokinetic-pharmacodynamic model that could simultaneously describe the interrelationship between tesaglitazar exposure and reduction in renal function over time in patients with type 2 diabetes mellitus. The pharmacokinetic-pharmacodynamic model could adequately describe the interplay between tesaglitazar and glomerular filtration rate. A one-compartment model in which the apparent clearance was influenced by glomerular filtration rate characterized the pharmacokinetics of tesaglitazar. An indirect-response model was used for the slow time course of change in glomerular filtration rate, which decreased from 100 to 78 mL/min/1.73m(2) after 12 weeks of treatment. All tesaglitazar-treated patients had a reduction in glomerular filtration rate, and available demographic variables could not explain differences in response. Patients treated with an angiotensin converting enzyme inhibitor were more sensitive to tesaglitazar and had larger glomerular filtration rate decrease compared to nontreated patients. Approximately 8 weeks after discontinuing treatment, mean glomerular filtration rate had returned towards baseline. The model and data give valuable insights into the dynamic changes in glomerular filtration rate over time.

Development of a complex parent-metabolite joint population pharmacokinetic model

This study aimed to develop a joint population pharmacokinetic model for an antipsychotic agent in development (S33138) and its active metabolite (S35424) produced by reversible metabolism. Because such a model leads to identifiability problems and numerical difficulties, the model building was performed using the FOCE-I and the Stochastic Approximation Expectation Maximization (SAEM) estimation algorithms in NONMEM and MONOLIX, respectively. Four different structural models were compared based on Bayesian information criteria. Models were first written as ordinary differential equations systems and then in closed form (CF) to facilitate further analyses. The impact of polymorphisms on genes coding for the CYP2C19 and CYP2D6 enzymes, respectively involved in the parent drug and the metabolite elimination were investigated using permutation Wald test. The parent drug and metabolite plasma concentrations of 101 patients were analyzed on two occasions after 4 and 8 weeks of treatment at 1, 3, 6, and 24 h following daily oral administration. All configurations led to a two compartment model with back-transformation of the metabolite into the parent drug and a first-pass effect. The elimination clearance of the metabolite through other processes than back-transformation was decreased by 35% [9-53%] in CYP2D6 poor metabolizer. Permutation tests were performed to ensure the robustness of the analysis, using SAEM and CF. In conclusion, we developed a complex joint pharmacokinetic model adequately predicting the impact of CYP2D6 polymorphisms on the parent drug and its metabolite concentrations through the back-transformation mechanism.

Importance of shrinkage in empirical bayes estimates for diagnostics: problems and solutions

Empirical Bayes ("post hoc") estimates (EBEs) of etas provide modelers with diagnostics: the EBEs themselves, individual prediction (IPRED), and residual errors (individual weighted residual (IWRES)). When data are uninformative at the individual level, the EBE distribution will shrink towards zero (eta-shrinkage, quantified as 1-SD(eta (EBE))/omega), IPREDs towards the corresponding observations, and IWRES towards zero (epsilon-shrinkage, quantified as 1-SD(IWRES)). These diagnostics are widely used in pharmacokinetic (PK) pharmacodynamic (PD) modeling; we investigate here their usefulness in the presence of shrinkage. Datasets were simulated from a range of PK PD models, EBEs estimated in non-linear mixed effects modeling based on the true or a misspecified model, and desired diagnostics evaluated both qualitatively and quantitatively. Identified consequences of eta-shrinkage on EBE-based model diagnostics include non-normal and/or asymmetric distribution of EBEs with their mean values ("ETABAR") significantly different from zero, even for a correctly specified model; EBE-EBE correlations and covariate relationships may be masked, falsely induced, or the shape of the true relationship distorted. Consequences of epsilon-shrinkage included low power of IPRED and IWRES to diagnose structural and residual error model misspecification, respectively. EBE-based diagnostics should be interpreted with caution whenever substantial eta- or epsilon-shrinkage exists (usually greater than 20% to 30%). Reporting the magnitude of eta- and epsilon-shrinkage will facilitate the informed use and interpretation of EBE-based diagnostics.

Evaluation of the nonparametric estimation method in NONMEM VI: application to real data

The aim of the study was to evaluate the nonparametric estimation methods available in NONMEM VI in comparison with the parametric first-order method (FO) and the first-order conditional estimation method (FOCE) when applied to real datasets. Four methods for estimating model parameters and parameter distributions (FO, FOCE, nonparametric preceded by FO (FO-NONP) and nonparametric preceded by FOCE (FOCE-NONP)) were compared for 25 models previously developed using real data and a parametric method. Numerical predictive checks were used to test the appropriateness of each model. Up to 1000 new datasets were simulated from each model and with each method to construct 90% and 50% prediction intervals. The mean absolute error and the mean error of the different outcomes investigated were computed as indicators of imprecision and bias respectively and formal statistical tests were performed. Overall, less imprecision and less bias were observed with nonparametric methods than with parametric methods. Across the 25 models, t-tests revealed that imprecision and bias were significantly lower (P < 0.05) with FOCE-NONP than with FOCE for half of the NPC outcomes investigated. Improvements were even more pronounced with FO-NONP in comparison with FO. In conclusion, when applied to real datasets and evaluated by numerical predictive checks, the nonparametric estimation methods in NONMEM VI performed better than the corresponding parametric methods (FO or FOCE).