Pediatric Population Pharmacokinetic Modeling and Exposure-Response Analysis of Ambrisentan in Pulmonary Arterial Hypertension and Comparison With Adult Data.

This study aimed to develop a population pharmacokinetic (PK) model of ambrisentan in pediatric patients (8 to <18 years) with pulmonary arterial hypertension (PAH) and compare pediatric ambrisentan systemic exposure with previously reported adult data. Association of ambrisentan exposure with efficacy (6-minute walking distance) and safety (adverse events) were exploratory analyses. A population PK model was developed using pediatric PK data. Steady-state systemic exposure metrics were estimated for the pediatric population and compared with previously reported data in adult patients with PAH and healthy subjects. No covariates had a significant effect on PK parameters; therefore, the final covariate model was the same as the base model. The pediatric population PK model was a 2-compartment model including the effect of body weight (allometric scaling), first-order absorption and elimination, and absorption lag time. Steady-state ambrisentan exposure was similar between the pediatric and adult population when accounting for body weight differences. Geometric mean area under the concentration-time curve at steady state in pediatric patients receiving ambrisentan low dose was 3% lower than in the adult population (and similar in both populations receiving high dose). Geometric mean maximum plasma concentration at steady state in pediatric patients receiving low and high doses was 11% and 18% higher, respectively, than in the adult population. There was no apparent association in the pediatric or adult population between ambrisentan exposure and change in 6-minute walking distance or incidence of ambrisentan-related adverse events in pediatric patients. The similar ambrisentan exposure and exposure-response profiles observed in pediatric and adult populations with PAH suggests appropriateness of body-weight-based dosing in the pediatric population with PAH

Selective androgen receptor modulation for muscle weakness in chronic obstructive pulmonary disease: a randomised control trial

Background Selective androgen receptor modulators (SARMs) increase muscle mass via the androgen receptor. This phase 2A trial investigated the effects of a SARM, GSK2881078, in conjunction with exercise, on leg strength in patients with chronic obstructive pulmonary disease (COPD) and impaired physical function. Methods 47 postmenopausal women and 50 men with COPD (forced expiratory volume in 1 s 30%–65% predicted; short physical performance battery score: 3–11) were enrolled into a randomised double-blind, placebo control trial. Patients were randomised 1:1 to once daily placebo or oral GSK2881078 (females: 1.0 mg; males: 2.0 mg) for 13 weeks with a concurrent home-exercise programme, involving strength training and physical activity. Primary endpoints were change from baseline in leg strength at 90 days (one-repetition maximum; absolute (kg) and relative (% change)) and multiple safety outcomes. Secondary endpoints included lean body mass, physical function and patient-reported outcomes. Results GSK2881078 increased leg strength in men. The difference in adjusted mean change from baseline and adjusted mean percentage change from baseline between treatment and placebo were: for women, 8.0 kg (90% CI −2.5 to 18.4) and 5.2% (90% CI −4.7 to 15.0), respectively; for men, 11.8 kg (90% CI −0.5 to 24.0) and 7.0% (90% CI 0.5 to 13.6), respectively. Lean body mass increased, but no changes in patient-reported outcomes were observed. Reversible reductions in high-density lipoprotein-cholesterol and transient elevations in hepatic transaminases were the main treatment-related safety findings. Conclusions GSK2881078 was well tolerated and short-term treatment increased leg strength, when expressed as per cent predicted, in men with COPD more than physical training alone

A Novel Method for Analysing Frequent Observations from Questionnaires in Order to Model Patient-Reported Outcomes : Application to EXACT (R) Daily Diary Data from COPD Patients

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease with approximately 174 million cases worldwide. Electronic questionnaires are increasingly used for collecting patient-reported-outcome (PRO) data about disease symptoms. Our aim was to leverage PRO data, collected to record COPD disease symptoms, in a general modelling framework to enable interpretation of PRO observations in relation to disease progression and potential to predict exacerbations. The data were collected daily over a year, in a prospective, observational study. The e-questionnaire, the EXAcerbations of COPD Tool (EXACT (R)) included 14 items (i.e. questions) with 4 or 5 ordered categorical response options. An item response theory (IRT) model was used to relate the responses from each item to the underlying latent variable (which we refer to as disease severity), and on each item level, Markov models (MM) with 4 or 5 categories were applied to describe the dependence between consecutive observations. Minimal continuous time MMs were used and parameterised using ordinary differential equations. One hundred twenty-seven COPD patients were included (median age 67years, 54% male, 39% current smokers), providing approximately 40,000 observations per EXACT (R) item. The final model suggested that, with time, patients more often reported the same scores as the previous day, i.e. the scores were more stable. The modelled COPD disease severity change over time varied markedly between subjects, but was small in the typical individual. This is the first IRT model with Markovian properties; our analysis proved them necessary for predicting symptom-defined exacerbations

Population model of longitudinal FEV1 data in asthmatics: meta-analysis and predictability of placebo response

Asthma is an obstructive lung disease where the mechanism of disease progression is not fully understood hence motivating the use of empirical models to describe the evolution of the patient’s health state. With reference to placebo response, measured in terms of FEV1 (Forced Expiratory Volume in 1 s), a range of empirical models taken from the literature were compared at a single trial level. In particular, eleven GSK trials lasting 12 weeks in mild-to-moderate asthma were used for the modelling of longitudinal placebo responses. Then, the chosen exponential model was used to carry out an individual participant data meta-analysis on eleven trials. A covariate analysis was also performed to find relevant covariates in asthma to be accounted for in the meta-analysis model. Age, gender, and height were found statistically significant (e.g. the taller the patients the higher the FEV1, the older the patients the lower the FEV1, and females have lower FEV1). By truncating each trial at week 4, the predictive properties of the meta-analysis model were also investigated, showing its ability to predict long-term FEV1 response from truncated trials. Summarizing, the study suggests that: (i) the exponential model effectively describes the placebo response; (ii) the meta-analysis approach may prove helpful to simulate new trials as well as to reduce trial duration in view of its predictive properties; (iii) the inclusion of available covariates within the meta-analysis model provides a reduction of the inter-individual variability

Joint longitudinal model-based meta-analysis of FEV1 and exacerbation rate in randomized COPD trials

Model-based meta-analysis (MBMA) is an approach that integrates relevant summary level data from heterogeneously designed randomized controlled trials (RCTs). This study not only evaluated the predictability of a published MBMA for forced expiratory volume in one second (FEV1) and its link to annual exacerbation rate in patients with chronic obstructive pulmonary disease (COPD) but also included data from new RCTs. A comparative effectiveness analysis across all drugs was also performed. Aggregated level data were collected from RCTs published between July 2013 and November 2020 (n = 132 references comprising 156 studies) and combined with data used in the legacy MBMA (published RCTs up to July 2013 - n = 142). The augmented data (n = 298) were used to evaluate the predictive performance of the published MBMA using goodness-of-fit plots for assessment. Furthermore, the model was extended including drugs that were not available before July 2013, estimating a new set of parameters. The legacy MBMA model predicted the post-2013 FEV1 data well, and new estimated parameters were similar to those of drugs in the same class. However, the exacerbation model overpredicted the post-2013 mean annual exacerbation rate data. Inclusion of year when the study started on the pre-treatment placebo rate improved the model predictive performance perhaps explaining potential improvements in the disease management over time. The addition of new data to the legacy COPD MBMA enabled a more robust model with increased predictability performance for both endpoints FEV1 and mean annual exacerbation rate

First-order longitudinal population model of FEV1 data: single-trial modeling and meta-analysis

Objectives: Asthma is a complex and multi-factorial disease and the underlying physiopathological mechanism is not completely known. Therefore, empirical models are usually adopted to describe the evolution of the patient's health state. The first objective of this work is to develop a parsimonious population model to describe the time course of placebo response. The clinical response is measured by the Forced Expiratory Volume in the first second (FEV1). The second objective is to perform a model-based meta-analysis, in order to assess differences among studies and to estimate the inter-trial variability. Methods: Placebo FEV1 longitudinal data from 11 clinical trials in subjects with mild-to-moderate asthma were available. All studies lasted 12 weeks. A parametric first-order response model was developed and identified on each dataset. Based on a single-trial analysis, the proposed model was compared to the linear, polynomial, Inverse Bateman and Weibull-and-Linear models. All the models were implemented in WinBUGS 1.4.3 [1] and compared through the Deviance Information Criterion (DIC). The best model was then adopted to perform a meta-analysis on the 11 datasets together. In the meta-analysis model, each individual parameter was defined as the sum of a term relative to the subject and one relative to the study. For both the single-trial analysis and the meta-analysis, log-normal distribution was assumed for all the parameters. Graphical outputs were obtained through R 2.13.1 [2]. Results: In the single-trial analysis, the first-order parametric model here proposed yielded the best performance in terms of DIC in most cases. Good individual fittings and Visual Predictive Checks were obtained for all the 11 trials. Hence, meta-analysis was performed. The proposed model yielded good performances also when applied in a meta-analysis context. Moreover, it was found that the inter-individual variability in each study is higher than the inter-trial one (baseline: 24% vs 6%; maximal response: 148% vs 28%; time constant: 906% vs 71%). Conclusion: A parsimonious parametric model able to describe FEV1 data from different studies in mild-to-moderate asthma was developed. The proposed model performs well both in the single-trial analysis and meta-analysis context. Moreover, the model can be extended by including clinically relevant covariates which may affect the patient's health state. A further work is to assess the model capabilities in predicting long-term outcomes from short-term trials in placebo group. References: [1] D.J. Lunn, A. Thomas, N. Best and D. Spiegelhalter, WinBUGS A Bayesian modelling framework: concepts, structure and extensibility, Statistics and Computing 10, 325-337, 2000 [2] R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2011). http://www.R-project.org

Improved Decision-Making Confidence Using Item-Based Pharmacometric Model : Illustration with a Phase II Placebo-Controlled Trial

This study aimed to illustrate how a new methodology to assess clinical trial outcome measures using a longitudinal item response theory-based model (IRM) could serve as an alternative to mixed model repeated measures (MMRM). Data from the EXACT (Exacerbation of chronic pulmonary disease tool) which is used to capture frequency, severity, and duration of exacerbations in COPD were analyzed using an IRM. The IRM included a graded response model characterizing item parameters and functions describing symptom-time course. Total scores were simulated (month 12) using uncertainty in parameter estimates. The 50th (2.5th, 97.5th) percentiles of the resulting simulated differences in average total score (drug minus placebo) represented the estimated drug effect (95%CI), which was compared with published MMRM results. Furthermore, differences in sample size, sensitivity, specificity, and type I and II errors between approaches were explored. Patients received either oral danirixin 75 mg twice daily (n=45) or placebo (n=48) on top of standard of care over 52 weeks. A step function best described the COPD symptoms-time course in both trial arms. The IRM improved precision of the estimated drug effect compared to MMRM, resulting in a sample size of 2.5 times larger for the MMRM analysis to achieve the IRM precision. The IRM showed a higher probability of a positive predictive value (34%) than MMRM (22%). An item model-based analysis data gave more precise estimates of drug effect than MMRM analysis for the same endpoint in this one case study