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Influence of the Size of Cohorts in Adaptive Design for Nonlinear Mixed Effects Models: An Evaluation by Simulation for a Pharmacokinetic and Pharmacodynamic Model for a Biomarker in Oncology

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Abstract

Purpose

In this study we aimed to evaluate adaptive designs (ADs) by clinical trial simulation for a pharmacokinetic-pharmacodynamic model in oncology and to compare them with one-stage designs, i.e., when no adaptation is performed, using wrong prior parameters.

Methods

We evaluated two one-stage designs, ξ0 and ξ*, optimised for prior and true population parameters, Ψ0 and Ψ*, and several ADs (two-, three- and five-stage). All designs had 50 patients. For ADs, the first cohort design was ξ0. The next cohort design was optimised using prior information updated from the previous cohort. Optimal design was based on the determinant of the Fisher information matrix using PFIM. Design evaluation was performed by clinical trial simulations using data simulated from Ψ*.

Results

Estimation results of two-stage ADs and ξ * were close and much better than those obtained with ξ 0. The balanced two-stage AD performed better than two-stage ADs with different cohort sizes. Three- and five-stage ADs were better than two-stage with small first cohort, but not better than the balanced two-stage design.

Conclusions

Two-stage ADs are useful when prior parameters are unreliable. In case of small first cohort, more adaptations are needed but these designs are complex to implement.

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Abbreviations

AD:

Adaptive design

FIM:

Fisher information matrix

NLMEM:

Nonlinear mixed effects model

PD:

Pharmacodynamic

PK:

Pharmacokinetic

REE:

Relative estimation error

RRMSE:

Relative root mean squared error

TGF-β:

Transforming growth factor β

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ACKNOWLEDGMENTS AND DISCLOSURES

The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n° 115156, resources of which are composed of financial contributions from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. The DDMoRe project is also financially supported by contributions from Academic and SME partners. This work does not necessarily represent the view of all DDMoRe partners.

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Authors and Affiliations

  1. IAME, UMR 1137, INSERM, Université Paris Diderot, Sorbonne Paris Cité, F-75018, Paris, France

    Giulia Lestini, Cyrielle Dumont & France Mentré

Authors
  1. Giulia Lestini
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  2. Cyrielle Dumont
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  3. France Mentré
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Correspondence to Giulia Lestini.

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Lestini, G., Dumont, C. & Mentré, F. Influence of the Size of Cohorts in Adaptive Design for Nonlinear Mixed Effects Models: An Evaluation by Simulation for a Pharmacokinetic and Pharmacodynamic Model for a Biomarker in Oncology. Pharm Res 32, 3159–3169 (2015). https://doi.org/10.1007/s11095-015-1693-3

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  • DOI: https://doi.org/10.1007/s11095-015-1693-3

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