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An example of optimal phase II design for exposure response modelling
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
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2010 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 37, no 5, 475-491 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents an example of how optimal design methodology was used to help design a phase II clinical study. The planned analysis would relate the clinical endpoint to exposure (measured via the area under the curve (AUC)), rather than dose. Optimal design methodology was used to compare a number of candidate phase II designs, and an algorithm for finding optimal designs was employed. The sigmoidal E-max with baseline (E-0) model was used to relate the clinical endpoint to individual subject AUCs, and the primary metrics were D optimality and the standard error (SE) of the AUC required to yield a clinically relevant change in the clinical endpoint. The performance of the candidate designs were compared across four different 'true' exposure response relationships (determined from the analysis of an earlier proof of concept (PoC) study). The results suggested the total sample size should be increased from the planned 540 individuals, and that the optimal design with 700 individuals would be equivalent to 812 individuals with the reference design (a 16% gain). The performance with this design was considered acceptable, although all designs performed poorly if the true exposure response relationship was very flat. This work allowed a prospective assessment of the likely performance and precision from the exposure response modelling prior to the start of the phase II study, and hence allowed the design to be revised to ensure the subsequent analysis would be of most value.

Place, publisher, year, edition, pages
2010. Vol. 37, no 5, 475-491 p.
Keyword [en]
Exposure response, Optimal design, E-max model, Phase II clinical trial
National Category
Pharmaceutical Sciences
URN: urn:nbn:se:uu:diva-134140DOI: 10.1007/s10928-010-9168-yISI: 000282873500002PubMedID: 20872056OAI: oai:DiVA.org:uu-134140DiVA: diva2:372167
Available from: 2010-11-24 Created: 2010-11-22 Last updated: 2013-01-23Bibliographically approved
In thesis
1. Optimal (Adaptive) Design and Estimation Performance in Pharmacometric Modelling
Open this publication in new window or tab >>Optimal (Adaptive) Design and Estimation Performance in Pharmacometric Modelling
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The pharmaceutical industry now recognises the importance of the newly defined discipline of pharmacometrics. Pharmacometrics uses mathematical models to describe and then predict the performance of new drugs in clinical development. To ensure these models are useful, the clinical studies need to be designed such that the data generated allows the model predictions to be sufficiently accurate and precise. The capability of the available software to reliably estimate the model parameters must also be well understood. 

This thesis investigated two important areas in pharmacometrics: optimal design and software estimation performance. The three optimal design papers progressed significant areas of optimal design research, especially relevant to phase II dose response designs. The use of exposure, rather than dose, was investigated within an optimal design framework. In addition to using both optimal design and clinical trial simulation, this work employed a wide range of metrics for assessing design performance, and was illustrative of how optimal designs for exposure response models may yield dose selections quite different to those based on standard dose response models. The investigation of the optimal designs for Poisson dose response models demonstrated a novel mathematical approach to the necessary matrix calculations for non-linear mixed effects models. Finally, the enormous potential of using optimal adaptive designs over fixed optimal designs was demonstrated. The results showed how the adaptive designs were robust to initial parameter misspecification, with the capability to "learn" the true dose response using the accruing subject data. The two estimation performance papers investigated the relative performance of a number of different algorithms and software programs for two complex pharmacometric models.

In conclusion these papers, in combination, cover a wide spectrum of study designs for non-linear dose/exposure response models, covering: normal/non-normal data, fixed/mixed effect models, single/multiple design criteria metrics, optimal design/clinical trial simulation, and adaptive/fixed designs. 

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2012. 76 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 166
Phase II, dose response, optimal design, adaptive design, exposure response, count data
National Category
Pharmaceutical Sciences
urn:nbn:se:uu:diva-182284 (URN)978-91-554-8491-0 (ISBN)
Public defence
2012-11-30, B41, Biomedicinskt Centrum, Husargatan 3, Uppsala, 09:15 (English)
Available from: 2012-11-02 Created: 2012-10-08 Last updated: 2013-01-23Bibliographically approved

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Simonsson, Ulrika S H
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