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PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
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2012 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 108, no 2, 789-805 p.Article in journal (Refereed) Published
Abstract [en]

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 theoptimal design software PopED, which incorporates many of these recent advances into aneasily 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 designperformance. 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. 

Place, publisher, year, edition, pages
2012. Vol. 108, no 2, 789-805 p.
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-160475DOI: 10.1016/j.cmpb.2012.05.005ISI: 000310828200030OAI: oai:DiVA.org:uu-160475DiVA: diva2:451146
Available from: 2011-10-24 Created: 2011-10-24 Last updated: 2017-12-08
In thesis
1. Practical Optimal Experimental Design in Drug Development and Drug Treatment using Nonlinear Mixed Effects Models
Open this publication in new window or tab >>Practical Optimal Experimental Design in Drug Development and Drug Treatment using Nonlinear Mixed Effects Models
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The cost of releasing a new drug on the market has increased rapidly in the last decade. The reasons for this increase vary with the drug, but the need to make correct decisions earlier in the drug development process and to maximize the information gained throughout the process is evident.

Optimal experimental design (OD) describes the procedure of maximizing relevant information in drug development and drug treatment processes. While various optimization criteria can be considered in OD, the most common is to optimize the unknown model parameters for an upcoming study. To date, OD has mainly been used to optimize the independent variables, e.g. sample times, but it can be used for any design variable in a study.

This thesis addresses the OD of multiple continuous or discrete design variables for nonlinear mixed effects models. The methodology for optimizing and the optimization of different types of models with either continuous or discrete data are presented and the benefits of OD for such models are shown. A software tool for optimizing these models in parallel is developed and three OD examples are demonstrated: 1) optimization of an intravenous glucose tolerance test resulting in a reduction in the number of samples by a third, 2) optimization of drug compound screening experiments resulting in the estimation of nonlinear kinetics and 3) an individual dose-finding study for the treatment of children with ciclosporin before kidney transplantation resulting in a reduction in the number of blood samples to ~27% of the original number and an 83% reduction in the study duration.

This thesis uses examples and methodology to show that studies in drug development and drug treatment can be optimized using nonlinear mixed effects OD. This provides a tool than can lower the cost and increase the overall efficiency of drug development and drug treatment.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2011. 74 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 149
Keyword
Pharmacometrics, optimal design, nonlinear mixed effects models, robust design, optimizing drug development, population models
National Category
Pharmaceutical Sciences
Research subject
Pharmacokinetics and Drug Therapy
Identifiers
urn:nbn:se:uu:diva-160481 (URN)978-91-554-8202-2 (ISBN)
Public defence
2011-12-09, B21, Biomedicinskt Centrum, Husargatan 3, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2011-11-18 Created: 2011-10-24 Last updated: 2011-11-23Bibliographically approved
2. Novel Pharmacometric Methods for Design and Analysis of Disease Progression Studies
Open this publication in new window or tab >>Novel Pharmacometric Methods for Design and Analysis of Disease Progression Studies
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

With societies aging all around the world, the global burden of degenerative diseases is expected to increase exponentially. From the perspective drug development, degenerative diseases represent an especially challenging class. Clinical trials, in this context often termed disease progression studies, are long, costly, require many individuals, and have low success rates. Therefore, it is crucial to use informative study designs and to analyze efficiently the obtained trial data. The development of novel approaches intended towards facilitating both the design and the analysis of disease progression studies was the aim of this thesis.

This aim was pursued in three stages (i) the characterization and extension of pharmacometric software, (ii) the development of new methodology around statistical power, and (iii) the demonstration of application benefits.

The optimal design software PopED was extended to simplify the application of optimal design methodology when planning a disease progression study. The performance of non-linear mixed effect estimation algorithms for trial data analysis was evaluated in terms of bias, precision, robustness with respect to initial estimates, and runtime. A novel statistic allowing for explicit optimization of study design for statistical power was derived and found to perform superior to existing methods. Monte-Carlo power studies were accelerated through application of parametric power estimation, delivering full power versus sample size curves from a few hundred Monte-Carlo samples. Optimal design and an explicit optimization for statistical power were applied to the planning of a study in Alzheimer's disease, resulting in a 30% smaller study size when targeting 80% power. The analysis of ADAS-cog score data was improved through application of item response theory, yielding a more exact description of the assessment score, an increased statistical power and an enhanced insight in the assessment properties.

In conclusion, this thesis presents novel pharmacometric methods that can help addressing the challenges of designing and planning disease progression studies.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2014. 65 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 184
Keyword
pharmacometrics, optimal design, non-linear mixed effects models, degenerative diseases, Alzheimer's disease, item response theory, statistical power
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-216537 (URN)978-91-554-8862-8 (ISBN)
Public defence
2014-03-07, B41, Biomedicinskt Centrum, Husargatan 3, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2014-02-13 Created: 2014-01-22 Last updated: 2014-04-29
3. Applied Adaptive Optimal Design and Novel Optimization Algorithms for Practical Use
Open this publication in new window or tab >>Applied Adaptive Optimal Design and Novel Optimization Algorithms for Practical Use
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The costs of developing new pharmaceuticals have increased dramatically during the past decades. Contributing to these increased expenses are the increasingly extensive and more complex clinical trials required to generate sufficient evidence regarding the safety and efficacy of the drugs.  It is therefore of great importance to improve the effectiveness of the clinical phases by increasing the information gained throughout the process so the correct decision may be made as early as possible.   Optimal Design (OD) methodology using the Fisher Information Matrix (FIM) based on Nonlinear Mixed Effect Models (NLMEM) has been proven to serve as a useful tool for making more informed decisions throughout the clinical investigation. The calculation of the FIM for NLMEM does however lack an analytic solution and is commonly approximated by linearization of the NLMEM. Furthermore, two structural assumptions of the FIM is available; a full FIM and a block-diagonal FIM which assumes that the fixed effects are independent of the random effects in the NLMEM. Once the FIM has been derived, it can be transformed into a scalar optimality criterion for comparing designs. The optimality criterion may be considered local, if the criterion is based on singe point values of the parameters or global (robust), where the criterion is formed for a prior distribution of the parameters.  Regardless of design criterion, FIM approximation or structural assumption, the design will be based on the prior information regarding the model and parameters, and is thus sensitive to misspecification in the design stage.  Model based adaptive optimal design (MBAOD) has however been shown to be less sensitive to misspecification in the design stage.   The aim of this thesis is to further the understanding and practicality when performing standard and MBAOD. This is to be achieved by: (i) investigating how two common FIM approximations and the structural assumptions may affect the optimized design, (ii) reducing runtimes complex design optimization by implementing a low level parallelization of the FIM calculation, (iii) further develop and demonstrate a framework for performing MBAOD, (vi) and investigate the potential advantages of using a global optimality criterion in the already robust MBAOD.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. 80 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 224
Keyword
Nonlinear Mixed Effects Models, Pharmacometrics, Fisher Information Matrix, Approximation, Optimality Criterion, Parallelization, Model Based Adaptive Optimal Design
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-308452 (URN)978-91-554-9766-8 (ISBN)
Public defence
2017-01-20, B22, BMC, Husargatan 3, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2016-12-21 Created: 2016-11-26 Last updated: 2016-12-21

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Nyberg, JoakimUeckert, SebastianStrömberg, EricKarlsson, Mats O.Hooker, Andrew C.

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