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Novel Pharmacometric Methods for Design and Analysis of Disease Progression Studies
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (​Pharmacometrics Research Group)ORCID iD: 0000-0002-3712-0255
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 [en]
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: urn:nbn:se:uu:diva-216537ISBN: 978-91-554-8862-8 (print)OAI: oai:DiVA.org:uu-216537DiVA: diva2:690248
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
List of papers
1. PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool
Open this publication in new window or tab >>PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool
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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. 

National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-160475 (URN)10.1016/j.cmpb.2012.05.005 (DOI)000310828200030 ()
Available from: 2011-10-24 Created: 2011-10-24 Last updated: 2017-12-08
2. Evaluation of Bias, Precision, Robustness and Runtime for Estimation Methods in NONMEM 7
Open this publication in new window or tab >>Evaluation of Bias, Precision, Robustness and Runtime for Estimation Methods in NONMEM 7
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2014 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 41, no 3, 223-238 p.Article in journal (Refereed) Published
Abstract [en]

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 algorithms in addition to the classical algorithms. In this study, performance of the estimation algorithms 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 algorithms 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 algorithm giving the lowest bias and highest precision across models was importance sampling (IMP), closely followed by FOCE/LAPLACE and stochastic approximation expectation-maximization (SAEM). The algorithms relative robustness differed between models, but FOCE/LAPLACE was the most robust algorithm across models, followed by SAEM and IMP. FOCE/LAPLACE was also the algorithm with the shortest runtime for all models, followed by iterative two-stage (ITS). The Bayesian Markov Chain Monte Carlo method, used in this study for point estimation, performed worst in all tested metrics.

Keyword
NONMEM, estimation algorithms
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-216136 (URN)10.1007/s10928-014-9359-z (DOI)000338496300003 ()24801864 (PubMedID)
Available from: 2014-01-19 Created: 2014-01-19 Last updated: 2017-12-06Bibliographically approved
3. Optimizing disease progression study designs for drug effect discrimination
Open this publication in new window or tab >>Optimizing disease progression study designs for drug effect discrimination
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2013 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 40, no 5, 587-596 p.Article in journal (Refereed) Published
Abstract [en]

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.

Keyword
Optimal experimental design, Statistical power, Wald test, Disease progression studies
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-210213 (URN)10.1007/s10928-013-9331-3 (DOI)000325263800004 ()
Available from: 2013-11-04 Created: 2013-11-04 Last updated: 2017-12-06
4. Accelerating Monte-Carlo Power Studies through Parametric Power Estimation
Open this publication in new window or tab >>Accelerating Monte-Carlo Power Studies through Parametric Power Estimation
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Estimating the power of a future clinical study is a common problem in the drug development process. Within the framework of model based drug development this problem is solved through Monte-Carlo studies where numerous replicates of the trial are simulated and subsequently analysed. This process can be very time consuming due to the high number of replicates required to obtain a stable power estimate. Non-linear mixed effect models which are frequently used for the analysis of clinical trial data are especially problematic as they can have a run time of several hours.

A novel parametric power estimation (PPE) algorithm utilizing the theoretical distribution of the alternative hypothesis is presented in this work and compared to classical Monte-Carlo studies. The PPE algorithm estimates the unknown non-centrality parameter in the theoretical distribution from a limited number of Monte-Carlo simulation and estimations. Furthermore, from the estimated parameter a complete power versus sample size curve can be obtained analytically without additional simulations. The PPE and classical Monte-Carlo algorithms were compared for 3 different drug development examples.

For a single power calculation, given a specific sample size, the PPE algorithm provided accurate estimates for all investigated scenarios and required 2 times fewer samples than the pure Monte-Carlo method to achieve the same level of precision. Furthermore, from this single power calculation, the PPE method can derive an entire power curve (power versus sample size), drastically reducing run times for this computation. The power curves from the PPE algorithm were in excellent agreement with the curves obtained using classical Monte-Carlo techniques.

National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-216528 (URN)
Available from: 2014-01-22 Created: 2014-01-22 Last updated: 2014-04-29
5. Challenges and potential of optimal design in late phase clinical trials through application in Alzheimer’s disease
Open this publication in new window or tab >>Challenges and potential of optimal design in late phase clinical trials through application in Alzheimer’s disease
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Optimal design is a methodology that can be a valuable tool for the planning of clinical studies. Current applications however, are largely limited to early phases of the drug development process. The increasing complexity in late phase trials is a major reason why optimal design is not applied at these stages. This work uses the example of Alzheimer's disease to investigate challenges and potential of applying optimal design in late phase clinical trials.

Information from several sources was used to construct a disease progression model for Alzheimer's disease. The resulting model was used to optimize the study design of an Alzheimer's trial for three distinct metrics: maximal information, minimal number of samples and maximal power to detect a drug effect. Challenges encountered and addressed during the implementation included covariates, dropout and clinical constraints.

Depending on the optimization criterion used, the optimal designs had 35% a higher efficiency, needed 33% fewer samples to obtain the same amount of information or required 70% fewer individuals to achieve 80% power compared to the reference design.

Optimal design can improve the design and therefore reduce the costs of late phase trials. Several tools and techniques have been identified to address the main challenges connected to this application.

National Category
Pharmaceutical Sciences Probability Theory and Statistics
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-215618 (URN)
Available from: 2014-01-15 Created: 2014-01-15 Last updated: 2015-02-12
6. Improved Utilization of ADAS-cog Assessment Data through Item Response Theory based Pharmacometric Modeling
Open this publication in new window or tab >>Improved Utilization of ADAS-cog Assessment Data through Item Response Theory based Pharmacometric Modeling
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2014 (English)In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 31, no 8, 2152-2165 p.Article in journal (Refereed) Published
Abstract [en]

Purpose

This work investigates improved utilization of ADAS-cog data (the primaryoutcome in Alzheimer's disease (AD) trials of mild and moderate AD) by combiningpharmacometric modeling and item response theory (IRT).

Methods

A baseline IRT model characterizing the ADAS-cog was built based on datafrom 2744 individuals. Pharmacometric methods were used to extend the baseline IRTmodel to describe longitudinal ADAS-cog scores from an 18-month clinical study with322 patients. Sensitivity of the ADAS-cog items in different patient populations as wellas the power to detect a drug effect in relation to total score base methods wereassessed with IRT based models.

Results

IRT analysis was able to describe both total and item level baseline ADAS-cogdata. Longitudinal data were also well described. Differences in the informationcontent of the item level components could be quantitatively characterized and rankedfor mild cognitively impairment and mild AD populations. Based on clinical trialsimulations with a theoretical drug effect, the IRT method demonstrated a significantlyhigher power to detect drug effect compared to the traditional method of analysis.

Conclusion

A combined framework of IRT and pharmacometric modeling permits amore effective and precise analysis than total score based methods and thereforeincreases the value of ADAS-cog data.

Place, publisher, year, edition, pages
Springer, 2014
Keyword
Alzheimer's disease, Item response theory, ADAS-cog, pharmacometrics, nonlinear mixed effect models
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-216524 (URN)10.1007/s11095-014-1315-5 (DOI)000341712400026 ()
Funder
EU, FP7, Seventh Framework Programme, 115156
Available from: 2014-01-22 Created: 2014-01-22 Last updated: 2017-12-06Bibliographically approved

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