Sebastian Ueckert

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.

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

PurposeThis 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).MethodsA 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.ResultsIRT 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.ConclusionA 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.

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.

A novel BACE inhibitor (PF-05297909): A two-part adaptive design to evaluate safety, pharmacokinetics and pharmacodynamics for modifying beta-amyloid in a first-in-human study

BackgroundThe accumulation of amyloid beta (Aβ) peptides is believed to be a central contributor to the neurodegeneration seen in the Alzheimers disease (AD) brain. Given the central role of Aβ42 in AD pathogenesis, a therapeutic strategy to lower central Aβ42 (and Aβ40) levels via inhibition of BACE was adopted in a first in human trial in a 2-part adaptive design.MethodsPart 1 evaluated PF-05297909 plasma PK and the PK/PD relationship for the reduction of plasma Aβ40, Aβ42 and AβX levels; Part 2 evaluated the exposure-response relationship between PF-05297909 and CSF levels of Aβ40, Aβ42 and AβX. Sufficient safety and tolerability, plasma exposure and reduction in plasma Aβ were necessary to initiate Part 2. Part 1 was a sequential parallel group dose escalation (25, 100, 250 and 325 mg) with n=8 (6:2, active:placebo) healthy volunteers (HV) in each cohort. Part 2 consisted of 3 cohorts of n=8 (6:2, active:placebo) HV. Doses selected for Part 2 started with the highest safe dose in Part 1 and then adapted for subsequent cohorts. The PK/PD relationship between PF-05297909 and Aβ42 was determined using a non-linear mixed effects (NLME) analysis. The doses for Part 2 - cohort 2 and 3 were to be chosen to improve the relative standard error in the estimate of the BACE IC50 as quantified by evaluating the determinant of the Fisher information matrix for the NLME model.ResultsPF-05297909 was well-tolerated. Reduction in plasma Aβ (Aβ40 and Aβ42) was exposure related with an apparent maximum at the 250 mg dose with a greater duration of activity at the 325 mg dose of PF-05297909. A 325 mg dose was selected for Part 2 - cohorts 1 and 2 without further cohorts being run, as stopping criteria for futility were met following analysis of cohort 2. A PK/PD relationship in CSF was not observed.ConclusionsThe adaptive designed PF-05297909 FIH study allowed efficient testing of safety and of the PK/PD relationship between PF-05297909 exposure and Aβ (Aβ40 and Aβ42). PF-05297909 was safe and well tolerated in HV at exposures tested. A robust effect on plasma Aβ did not translate to CSF pharmacodynamic effects.

Application of Item Response Theory to Modeling of Expanded Disability Status Scale in Multiple Sclerosis

ABSTRACTIn this study, we report the development of the first item response theory (IRT) model within a pharmacometrics framework to characterize the disease progression in multiple sclerosis (MS), as measured by Expanded Disability Status Score (EDSS). Data were collected quarterly from a 96-week phase III clinical study by a blinder rater, involving 104,206 item-level observations from 1319 patients with relapsing-remitting MS (RRMS), treated with placebo or cladribine. Observed scores for each EDSS item were modeled describing the probability of a given score as a function of patients’ (unobserved) disability using a logistic model. Longitudinal data from placebo arms were used to describe the disease progression over time, and the model was then extended to cladribine arms to characterize the drug effect. Sensitivity with respect to patient disability was calculated as Fisher information for each EDSS item, which were ranked according to the amount of information they contained. The IRT model was able to describe baseline and longitudinal EDSS data on item and total level. The final model suggested that cladribine treatment significantly slows disease-progression rate, with a 20% decrease in disease-progression rate compared to placebo, irrespective of exposure, and effects an additional exposure-dependent reduction in disability progression. Four out of eight items contained 80% of information for the given range of disabilities. This study has illustrated that IRT modeling is specifically suitable for accurate quantification of disease status and description and prediction of disease progression in phase 3 studies on RRMS, by integrating EDSS item-level data in a meaningful manner.

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).

Tutorial: 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).

Modeling Composite Assessment Data Using Item Response Theory: Modeling Composite Assessment Data Using IRT

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.