Inhibition of Factor XI Using RBD4059: A Novel GalNAc-siRNA With Potent and Durable Antithrombotic Effects.

Coagulation factor XI (FXI) is an attractive target for novel antithrombotic drugs because of its minor role in hemostasis, which may address the current unmet medical need regarding a powerful anticoagulant with low bleeding risk. Here, we present RBD4059, the first FXI-targeting GalNAc-siRNA molecule to reach the clinical stage of development. In this study, RBD4059 demonstrated potent FXI inhibition with long duration in mice and monkeys, antithrombotic effects in mouse thrombosis models, and no prolonged bleeding in a mouse tail bleeding model.

A Model-Based Evaluation of Noninvasive Biomarkers to Reflect Histological Nonalcoholic Fatty Liver Disease Scores.

Background: Nonalcoholic fatty liver disease (NAFLD) comprises multiple heterogeneous pathophysiological conditions commonly evaluated by suboptimal liver biopsies. This study aimed to elucidate the role of 13 diverse histological liver scores in assessing NAFLD disease activity using an in silico pharmacometric model-based approach. We further sought to investigate various noninvasive patient characteristics for their ability to reflect all 13 histological scores and the NAFLD activity score (NAS).

Comparison of the power and type 1 error of total score models for drug effect detection in clinical trials.

Composite scale data consists of numerous categorical questions/items that are often summed as a total score and are commonly utilized as primary endpoints in clinical trials. These endpoints are conceptually discrete and constrained by nature. Item response theory (IRT) is a powerful approach for detecting drug effects in composite scale data from clinical trials, but estimating all parameters requires a large sample size and all item information, which may not be available.

Exposure-response modeling for nausea incidence for cotadutide using a Markov modeling approach.

Cotadutide is a dual glucagon-like peptide-1 (GLP-1)/glucagon receptor agonist. Gastrointestinal adverse effects are known to be associated with GLP-1 receptor agonism and can be mitigated through tolerance development via a gradual up-titration. This analysis aimed to characterize the relationship between exposure and nausea incidence and to optimize titration schemes.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.