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Trial treatment length optimization with an emphasis on disease progression studies
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)
2009 (English)In: Journal of clinical pharmacology, ISSN 0091-2700, E-ISSN 1552-4604, Vol. 49, no 3, 323-335 p.Article in journal (Refereed) Published
Abstract [en]

Optimal design has been used in the past mainly to optimize sampling schedules for clinical trials. Optimization on design variables other than sampling times has been published in the literature only once before. This study shows, as an example, optimization on the length of treatment periods to obtain reliable estimates of drug effects on longterm disease progression studies. Disease progression studies are high in cost, effort, and time; therefore, optimization of treatment length is highly recommended to avoid failure or loss of information. Results are provided for different drug effects (eg, protective and symptomatic) and for different lengths of studies and sampling schedules. The merits of extending the total study length versus inclusion of more samples per participants are investigated. The authors demonstrate that if no observations are taken during the washout period, a trial can lose up to 40% of its efficiency. Furthermore, when optimization of treatment length is performed using multiple possible drug effect models simultaneously, these studies show high power in discriminating between different drug effect models.

Place, publisher, year, edition, pages
2009. Vol. 49, no 3, 323-335 p.
Keyword [en]
optimal design, disease progression studies, clinical trial design, pharmacometrics
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-122158DOI: 10.1177/0091270008329560ISI: 000263695600007PubMedID: 19246730OAI: oai:DiVA.org:uu-122158DiVA: diva2:308511
Available from: 2010-04-06 Created: 2010-04-06 Last updated: 2017-12-12Bibliographically approved
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

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Hennig, StefanieHooker, Andrew CKarlsson, Mats O

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