Zibotentan in combination with dapagliflozin compared with dapagliflozin in patients with chronic kidney disease (ZENITH-CKD): a multicentre, randomised, active-controlled, phase 2b, clinical trial.

BACKGROUND: In patients with chronic kidney disease, SGLT2 inhibitors and endothelin A receptor antagonists (ERAs) can reduce albuminuria and glomerular filtration rate (GFR) decline. We assessed the albuminuria-lowering efficacy and safety of the ERA zibotentan combined with the SGLT2 inhibitor dapagliflozin. METHODS: ZENITH-CKD was a multicentre, randomised, double-blind, active-controlled clinical trial, done in 170 clinical practice sites in 18 countries. Adults (≥18 to ≤90 years) with an estimated GFR (eGFR) of 20 mL/min per 1·73 m2 or greater and a urinary albumin-to-creatinine ratio (UACR) of 150-5000 mg/g were randomly assigned (2:1:2) to 12 weeks of daily treatment with zibotentan 1·5 mg plus dapagliflozin 10 mg, zibotentan 0·25 mg plus dapagliflozin 10 mg, or dapagliflozin 10 mg plus placebo, as adjunct to angiotensin-converting enzyme inhibitors or angiotensin receptor blockers if tolerated. The primary endpoint was a change from baseline in log-transformed UACR (zibotentan 1·5 mg plus dapagliflozin vs dapagliflozin plus placebo) at week 12. Fluid retention was an event of special interest, defined as an increase in bodyweight of at least 3% (at least 2·5% must have been from total body water) from baseline or an increase of at least 100% in B-type natriuretic peptide (BNP) and either a BNP concentration greater than 200 pg/mL if without atrial fibrillation or BNP greater than 400 pg/mL if with atrial fibrillation. This trial is registered with ClinicalTrials.gov, NCT04724837, and is completed. FINDINGS: Between April 28, 2021, and Jan 17, 2023, we assessed 1492 participants for eligibility. For the main analysis, we randomly assigned 449 (30%) participants, 447 (99%) of whom (mean age 62·8 years [SD 12·1], 138 [31%] female, 309 [69%] male, 305 [68%] White, mean eGFR 46·7 mL/min per 1·73 m2 [SD 22·4], and median UACR 565·5 mg/g [IQR 243·0-1212·6]) received treatment with zibotentan 1·5 mg plus dapagliflozin (n=179 [40%]), zibotentan 0·25 mg plus dapagliflozin (n=91 [20%]), or dapagliflozin plus placebo (n=177 [40%]). Zibotentan 1·5 mg plus dapagliflozin and zibotentan 0·25 mg plus dapagliflozin reduced UACR versus dapagliflozin plus placebo throughout the treatment period of the study. At week 12, the difference in UACR versus dapagliflozin plus placebo was -33·7% (90% CI -42·5 to -23·5; p<0·0001) for zibotentan 1·5 mg plus dapagliflozin and -27·0% (90% CI -38·4 to -13·6; p=0·0022) for zibotentan 0·25 mg plus dapagliflozin. Fluid-retention events were observed in 33 (18%) of 179 participants in the zibotentan 1·5 mg plus dapagliflozin group, eight (9%) of 91 in the zibotentan 0·25 mg plus dapagliflozin group, and 14 (8%) of 177 in the dapagliflozin plus placebo group. INTERPRETATION: Zibotentan combined with dapagliflozin reduced albuminuria with an acceptable tolerability and safety profile and is an option to reduce chronic kidney disease progression in patients already receiving currently recommended therapy. FUNDING: AstraZeneca.

Pharmacokinetics and Tolerability of Zibotentan in Patients with Concurrent Moderate Renal and Moderate Hepatic Impairment.

BACKGROUND AND OBJECTIVE: Zibotentan, a selective endothelin A receptor antagonist, is in development for chronic liver and kidney disease. The pharmacokinetics (PK) of zibotentan were previously investigated in patients with either renal impairment or hepatic impairment, but the impact of both pathologies on PK was not evaluated. This study evaluated the PK and tolerability of a single oral dose of zibotentan in participants with concurrent moderate renal impairment and moderate hepatic impairment versus control participants. METHODS: Twelve participants with moderate renal and hepatic impairment and 11 healthy matched control participants with no clinically significant liver or kidney disease were enrolled in an open-label, parallel-group study design. After administration of a single oral dose of zibotentan 5 mg, blood and urine sampling was performed. Pharmacokinetic parameters were determined for each of the two cohorts and compared. Comparisons between the cohorts were based on the geometric least squares mean ratio for the primary endpoints, which were area under the plasma concentration-time curve (AUC) from time zero to infinity (AUC∞) and from time zero to the time of the last measurable concentration (AUClast), and maximum plasma drug concentration (Cmax) on Day 1 through 120 h post-dose. Secondary endpoints included apparent total body clearance (CL/F) on Day 1 through 120 h post-dose. Safety endpoints were assessed up to discharge. RESULTS: In total, 11 participants with concurrent moderate renal and hepatic impairment, and 11 controls, completed the study. Zibotentan was generally well tolerated, and no new clinically significant safety findings were observed. Total exposure (AUC∞ and AUClast) was approximately 2.10-fold higher in participants with concurrent moderate renal and hepatic impairment versus controls, while Cmax and total nonrenal body clearance were similar among all groups. A regression-based post hoc analysis, comparing exposure and CL/F in patients with concurrent impairment to patients with either renal or hepatic impairment alone, showed that CL/F with concurrent impairment was approximately half of that in controls and was positively correlated with reduction of renal function. Inclusion of the data on concurrent moderate renal and hepatic impairment in the regression analysis led to a narrower confidence interval for the predicted mean CL/F in participants with moderate hepatic impairment. CONCLUSION: The presented findings advance the understanding of the PK of zibotentan in both renal impairment and hepatic impairment, with and without overlapping pathologies, and will thus increase the confidence of dose selection in future studies, particularly in vulnerable patient populations with concurrent renal and hepatic impairment. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05112419.

Evaluation of the effect of erenumab on migraine-specific questionnaire in patients with chronic and episodic migraine.

Erenumab is a fully human anti-canonical calcitonin gene-related peptide receptor monoclonal antibody approved for migraine prevention. The Migraine-Specific Quality-of-Life Questionnaire (MSQ) is a 14-item patient-reported outcome instrument that measures the impact of migraine on health-related quality of life. Erenumab data from four phase II/III clinical trials were used to develop an item response theory (IRT) model within a nonlinear mixed effects framework, (i) evaluate the MSQ item information with respect to patient disability, (ii) characterize the longitudinal progression of the MSQ, and (iii) quantify the effect of erenumab on the MSQ in patients with migraine. The majority (80%) of information was found to be contained in 9 out of 14 items, extending the current knowledge on the reliability of the MSQ as a psychometric tool. Simulations across three MSQ domains show significant improvement from baseline, exceeding minimally important differences. Overall, the IRT model platform developed herein allows for systematic quantification of the effect of erenumab on the MSQ in patients with migraine.

A Markov model of fibrosis development in nonalcoholic fatty liver disease predicts fibrosis progression in clinical cohorts.

Disease progression in nonalcoholic steatohepatitis (NASH) is highly heterogenous and remains poorly understood. Fibrosis stage is currently the best predictor for development of end-stage liver disease and mortality. Better understanding and quantifying the impact of factors affecting NASH and fibrosis is essential to inform a clinical study design. We developed a population Markov model to describe the transition probability between fibrosis stages and mortality using a unique clinical nonalcoholic fatty liver disease cohort with serial biopsies over 3 decades. We evaluated covariate effects on all model parameters and performed clinical trial simulations to predict the fibrosis progression rate for external clinical cohorts. All parameters were estimated with good precision. Age and diagnosis of type 2 diabetes (T2D) were found to be significant predictors in the model. Increase in hepatic steatosis between visits was the most important predictor for progression of fibrosis. Fibrosis progression rate (FPR) was twofold higher for fibrosis stages 0 and 1 (F0-1) compared to fibrosis stage 2 and 3 (F2-3). A twofold increase in FPR was observed for T2D. A two-point steatosis worsening increased the FPR 11-fold. Predicted fibrosis progression was in good agreement with data from external clinical cohorts. Our fibrosis progression model shows that patient selection, particularly initial fibrosis stage distribution, can significantly impact fibrosis progression and as such the window for assessing drug efficacy in clinical trials. Our work highlights the increase in hepatic steatosis as the most important factor in increasing FPR, emphasizing the importance of well-defined lifestyle advise for reducing variability in NASH progression during clinical trials.

How to analyze continuous and discrete repeated measures in small-sample cross-over trials?

To optimize the use of data from a small number of subjects in rare disease trials, an at first sight advantageous design is the repeated measures cross-over design. However, it is unclear how these within-treatment period and within-subject clustered data are best analyzed in small-sample trials. In a real-data simulation study based upon a recent epidermolysis bullosa simplex trial using this design, we compare non-parametric marginal models, generalized pairwise comparison models, GEE-type models and parametric model averaging for both repeated binary and count data. The recommendation of which methodology to use in rare disease trials with a repeated measures cross-over design depends on the type of outcome and the number of time points the treatment has an effect on. The non-parametric marginal model testing the treatment-time-interaction effect is suitable for detecting between group differences in the shapes of the longitudinal profiles. For binary outcomes with the treatment effect on a single time point, the parametric model averaging method is recommended, while in the other cases the unmatched generalized pairwise comparison methodology is recommended. Both provide an easily interpretable effect size measure, and do not require exclusion of periods or subjects due to incompleteness.

An Item Response Theory-Informed Strategy to Model Total Score Data from Composite Scales.

Composite scale data is widely used in many therapeutic areas and consists of several categorical questions/items that are usually summarized into a total score (TS). Such data is discrete and bounded by nature. The gold standard to analyse composite scale data is item response theory (IRT) models. However, IRT models require item-level data while sometimes only TS is available. This work investigates models for TS. When an IRT model exists, it can be used to derive the information as well as expected mean and variability of TS at any point, which can inform TS-analyses. We propose a new method: IRT-informed functions of expected values and standard deviation in TS-analyses. The most common models for TS-analyses are continuous variable (CV) models, while bounded integer (BI) models offer an alternative that respects scale boundaries and the nature of TS data. We investigate the method in CV and BI models on both simulated and real data. Both CV and BI models were improved in fit by IRT-informed disease progression, which allows modellers to precisely and accurately find the corresponding latent variable parameters, and IRT-informed SD, which allows deviations from homoscedasticity. The methodology provides a formal way to link IRT models and TS models, and to compare the relative information of different model types. Also, joint analyses of item-level data and TS data are made possible. Thus, IRT-informed functions can facilitate total score analysis and allow a quantitative analysis of relative merits of different analysis methods.

cLRT-Mod: An efficient methodology for pharmacometric model-based analysis of longitudinal phase II dose finding studies under model uncertainty.

Within the challenging context of phase II dose-finding trials, longitudinal analyses may increase drug effect detection power compared to an end-of-treatment analysis. This work proposes cLRT-Mod, a pharmacometric adaptation of the MCP-Mod methodology, which allows the use of nonlinear mixed effect models to first detect a dose-response signal and then identify the doses for the confirmatory phase while accounting for model structure uncertainty. The method was evaluated through extensive clinical trial simulations of a hypothetical phase II dose-finding trial using different scenarios and comparing different methods such as MCP-Mod. The results show an increase in power using cLRT with longitudinal data compared to an EOT multiple contrast tests for scenarios with small sample size and weak drug effect while maintaining pre-specifiability of the models prior to data analysis and the nominal type I error. This work shows how model averaging provides better coverage probability of the drug effect in the prediction step, and avoids under-estimation of the size of the confidence interval. Finally, for illustration purpose cLRT-Mod was applied to the analysis of a real phase II dose-finding trial.

Improved numerical stability for the bounded integer model.

This article highlights some numerical challenges when implementing the bounded integer model for composite score modeling and suggests an improved implementation. The improvement is based on an approximation of the logarithm of the error function. After presenting the derivation of the improved implementation, the article compares the performance of the algorithm to a naive implementation of the log-likelihood using both simulations and a real data example. In the simulation setting, the improved algorithm yielded more precise and less biased parameter estimates when the within-subject variability was small and estimation was performed using the Laplace algorithm. The estimation results did not differ between implementations when the SAEM algorithm was used. For the real data example, bootstrap results differed between implementations with the improved implementation producing identical or better objective function values. Based on the findings in this article, the improved implementation is suggested as the new default log-likelihood implementation for the bounded integer model.

Performance of longitudinal item response theory models in shortened or partial assessments.

This work evaluates the performance of longitudinal item response (IR) theory models in shortened assessments using an existing model for part II and III of the MDS-UPDRS score. Based on the item information content, the assessment was reduced by removal of items in multiple increments and the models' ability to recover the item characteristics of the remaining items at each level was evaluated. This evaluation was done for both simulated and real data. The metric of comparison in both cases was the item information function. For real data, the impact of shortening on the estimated disease progression and drug effect was also studied. In the simulated data setting, the item characteristics did not differ between the full and the shortened assessments down to the lowest level of information remaining; indicating a considerable independence between items. In contrast when reducing the assessment in a real data setting, a substantial change in item information was observed for some of the items. Disease progression and drug effect estimates also decreased in the reduced assessments. These changes indicate a shift in the measured construct of the shortened assessment and warrant caution when comparing results from a partial assessment with results from the full assessment.

Model-Based Conditional Weighted Residuals Analysis for Structural Model Assessment.

Nonlinear mixed effects models are widely used to describe longitudinal data to improve the efficiency of drug development process or increase the understanding of the studied disease. In such settings, the appropriateness of the modeling assumptions is critical in order to draw correct conclusions and must be carefully assessed for any substantial violations. Here, we propose a new method for structure model assessment, based on assessment of bias in conditional weighted residuals (CWRES). We illustrate this method by assessing prediction bias in two integrated models for glucose homeostasis, the integrated glucose-insulin (IGI) model, and the integrated minimal model (IMM). One dataset was simulated from each model then analyzed with the two models. CWRES outputted from each model fitting were modeled to capture systematic trends in CWRES as well as the magnitude of structural model misspecifications in terms of difference in objective function values (ΔOFVBias). The estimates of CWRES bias were used to calculate the corresponding bias in conditional predictions by the inversion of first-order conditional estimation method's covariance equation. Time, glucose, and insulin concentration predictions were the investigated independent variables. The new method identified correctly the bias in glucose sub-model of the integrated minimal model (IMM), when this bias occurred, and calculated the absolute and proportional magnitude of the resulting bias. CWRES bias versus the independent variables agreed well with the true trends of misspecification. This method is fast easily automated diagnostic tool for model development/evaluation process, and it is already implemented as part of the Perl-speaks-NONMEM software.

Modeling Composite Assessment Data Using Item Response Theory.

Composite assessments aim to combine different aspects of a disease in a single score and are utilized in a variety of therapeutic areas. The data arising from these evaluations are inherently discrete with distinct statistical properties. This tutorial presents the framework of the item response theory (IRT) for the analysis of this data type in a pharmacometric context. The article considers both conceptual (terms and assumptions) and practical questions (modeling software, data requirements, and model building).

Comparison of Model Averaging and Model Selection in Dose Finding Trials Analyzed by Nonlinear Mixed Effect Models.

In drug development, pharmacometric approaches consist in identifying via a model selection (MS) process the model structure that best describes the data. However, making predictions using a selected model ignores model structure uncertainty, which could impair predictive performance. To overcome this drawback, model averaging (MA) takes into account the uncertainty across a set of candidate models by weighting them as a function of an information criterion. Our primary objective was to use clinical trial simulations (CTSs) to compare model selection (MS) with model averaging (MA) in dose finding clinical trials, based on the AIC information criterion. A secondary aim of this analysis was to challenge the use of AIC by comparing MA and MS using five different information criteria. CTSs were based on a nonlinear mixed effect model characterizing the time course of visual acuity in wet age-related macular degeneration patients. Predictive performances of the modeling approaches were evaluated using three performance criteria focused on the main objectives of a phase II clinical trial. In this framework, MA adequately described the data and showed better predictive performance than MS, increasing the likelihood of accurately characterizing the dose-response relationship and defining the minimum effective dose. Moreover, regardless of the modeling approach, AIC was associated with the best predictive performances.

Item Response Theory as an Efficient Tool to Describe a Heterogeneous Clinical Rating Scale in De Novo Idiopathic Parkinson's Disease Patients.

PURPOSE: This manuscript aims to precisely describe the natural disease progression of Parkinson's disease (PD) patients and evaluate approaches to increase the drug effect detection power. METHODS: An item response theory (IRT) longitudinal model was built to describe the natural disease progression of 423 de novo PD patients followed during 48 months while taking into account the heterogeneous nature of the MDS-UPDRS. Clinical trial simulations were then used to compare drug effect detection power from IRT and sum of item scores based analysis under different analysis endpoints and drug effects. RESULTS: The IRT longitudinal model accurately describes the evolution of patients with and without PD medications while estimating different progression rates for the subscales. When comparing analysis methods, the IRT-based one consistently provided the highest power. CONCLUSION: IRT is a powerful tool which enables to capture the heterogeneous nature of the MDS-UPDRS.

An MCMC method for the evaluation of the Fisher information matrix for non-linear mixed effect models.

Non-linear mixed effect models (NLMEMs) are widely used for the analysis of longitudinal data. To design these studies, optimal design based on the expected Fisher information matrix (FIM) can be used instead of performing time-consuming clinical trial simulations. In recent years, estimation algorithms for NLMEMs have transitioned from linearization toward more exact higher-order methods. Optimal design, on the other hand, has mainly relied on first-order (FO) linearization to calculate the FIM. Although efficient in general, FO cannot be applied to complex non-linear models and with difficulty in studies with discrete data. We propose an approach to evaluate the expected FIM in NLMEMs for both discrete and continuous outcomes. We used Markov Chain Monte Carlo (MCMC) to integrate the derivatives of the log-likelihood over the random effects, and Monte Carlo to evaluate its expectation w.r.t. the observations. Our method was implemented in R using Stan, which efficiently draws MCMC samples and calculates partial derivatives of the log-likelihood. Evaluated on several examples, our approach showed good performance with relative standard errors (RSEs) close to those obtained by simulations. We studied the influence of the number of MC and MCMC samples and computed the uncertainty of the FIM evaluation. We also compared our approach to Adaptive Gaussian Quadrature, Laplace approximation, and FO. Our method is available in R-package MIXFIM and can be used to evaluate the FIM, its determinant with confidence intervals (CIs), and RSEs with CIs.

Accelerating Monte Carlo power studies through parametric power estimation.

Estimating the power for a non-linear mixed-effects model-based analysis is challenging due to the lack of a closed form analytic expression. Often, computationally intensive Monte Carlo studies need to be employed to evaluate the power of a planned experiment. This is especially time consuming if full power versus sample size curves are to be obtained. A novel parametric power estimation (PPE) algorithm utilizing the theoretical distribution of the alternative hypothesis is presented in this work. The PPE algorithm estimates the unknown non-centrality parameter in the theoretical distribution from a limited number of Monte Carlo simulation and estimations. The estimated parameter linearly scales with study size allowing a quick generation of the full power versus study size curve. A comparison of the PPE with the classical, purely Monte Carlo-based power estimation (MCPE) algorithm for five diverse pharmacometric models showed an excellent agreement between both algorithms, with a low bias of less than 1.2 % and higher precision for the PPE. The power extrapolated from a specific study size was in a very good agreement with power curves obtained with the MCPE algorithm. PPE represents a promising approach to accelerate the power calculation for non-linear mixed effect models.

Evaluation of bias, precision, robustness and runtime for estimation methods in NONMEM 7.

NONMEM is the most widely used software for population pharmacokinetic (PK)-pharmacodynamic (PD) analyses. The latest version, NONMEM 7 (NM7), includes several sampling-based estimation methods in addition to the classical methods. In this study, performance of the estimation methods available in NM7 was investigated with respect to bias, precision, robustness and runtime for a diverse set of PD models. Simulations of 500 data sets from each PD model were reanalyzed with the available estimation methods to investigate bias and precision. Simulations of 100 data sets were used to investigate robustness by comparing final estimates obtained after estimations starting from the true parameter values and initial estimates randomly generated using the CHAIN feature in NM7. Average estimation time for each algorithm and each model was calculated from the runtimes reported by NM7. The method giving the lowest bias and highest precision across models was importance sampling, closely followed by FOCE/LAPLACE and stochastic approximation expectation-maximization. The methods relative robustness differed between models and no method showed clear superior performance. FOCE/LAPLACE was the method with the shortest runtime for all models, followed by iterative two-stage. The Bayesian Markov Chain Monte Carlo method, used in this study for point estimation, performed worst in all tested metrics.

Improved utilization of ADAS-cog assessment data through item response theory based pharmacometric modeling.

PURPOSE: This work investigates improved utilization of ADAS-cog data (the primary outcome in Alzheimer's disease (AD) trials of mild and moderate AD) by combining pharmacometric modeling and item response theory (IRT). METHODS: A baseline IRT model characterizing the ADAS-cog was built based on data from 2,744 individuals. Pharmacometric methods were used to extend the baseline IRT model to describe longitudinal ADAS-cog scores from an 18-month clinical study with 322 patients. Sensitivity of the ADAS-cog items in different patient populations as well as the power to detect a drug effect in relation to total score based methods were assessed with the IRT based model. RESULTS: IRT analysis was able to describe both total and item level baseline ADAS-cog data. Longitudinal data were also well described. Differences in the information content of the item level components could be quantitatively characterized and ranked for mild cognitively impairment and mild AD populations. Based on clinical trial simulations with a theoretical drug effect, the IRT method demonstrated a significantly higher power to detect drug effect compared to the traditional method of analysis. CONCLUSION: A combined framework of IRT and pharmacometric modeling permits a more effective and precise analysis than total score based methods and therefore increases the value of ADAS-cog data.

Optimizing disease progression study designs for drug effect discrimination.

Investigate the possibility to directly optimize a clinical trial design for statistical power to detect a drug effect and compare to optimal designs that focus on parameter precision. An improved statistic derived from the general formulation of the Wald approximation was used to predict the statistical power for given trial designs of a disease progression study. The predicted value was compared, together with the classical Wald statistic, to a type I error-corrected model-based power determined via clinical trial simulations. In a second step, a study design for maximal power was determined by directly maximizing the new statistic. The resulting power-optimal designs and their corresponding performance based on empirical power calculations were compared to designs focusing on parameter precision. Comparisons of empirically determined power and the newly developed statistic, showed excellent agreement across all scenarios investigated. This was in contrast to the classical Wald statistic, which consistently over-predicted the reference power with deviations of up to 90 %. Designs maximized using the proposed metric differed from traditional optimal designs and showed equal or up to 20 % higher power in the subsequent clinical trial simulations. Furthermore, the proposed method was used to minimize the number of individuals required to achieve 80 % power through a simultaneous optimization of study size and study design. The targeted power of 80 % was confirmed in subsequent simulation study. A new statistic was developed, allowing for the explicit optimization of a clinical trial design with respect to statistical power.

PopED: an extended, parallelized, nonlinear mixed effects models optimal design tool.

Several developments have facilitated the practical application and increased the general use of optimal design for nonlinear mixed effects models. These developments include new methodology for utilizing advanced pharmacometric models, faster optimization algorithms and user friendly software tools. In this paper we present the extension of the optimal design software PopED, which incorporates many of these recent advances into an easily useable enhanced GUI. Furthermore, we present new solutions to problems related to the design of experiments such as: faster and more robust FIM calculations and optimizations, optimizing over cost/utility functions and diagnostic tools and plots to evaluate design performance. Examples for; (i) Group size optimization and efficiency translation, (ii) Cost/constraint optimization, (iii) Optimizations with different FIM approximations and (iv) optimization with parallel computing demonstrate the new features in PopED and underline the potential use of this tool when designing experiments.