The AAPS Journal

, Volume 18, Issue 5, pp 1233–1243 | Cite as

Optimal Design for Informative Protocols in Xenograft Tumor Growth Inhibition Experiments in Mice

  • Giulia LestiniEmail author
  • France Mentré
  • Paolo Magni
Research Article


Tumor growth inhibition (TGI) models are increasingly used during preclinical drug development in oncology for the in vivo evaluation of antitumor effect. Tumor sizes are measured in xenografted mice, often only during and shortly after treatment, thus preventing correct identification of some TGI model parameters. Our aims were (i) to evaluate the importance of including measurements during tumor regrowth and (ii) to investigate the proportions of mice included in each arm. For these purposes, optimal design theory based on the Fisher information matrix implemented in PFIM4.0 was applied. Published xenograft experiments, involving different drugs, schedules, and cell lines, were used to help optimize experimental settings and parameters using the Simeoni TGI model. For each experiment, a two-arm design, i.e., control versus treatment, was optimized with or without the constraint of not sampling during tumor regrowth, i.e., “short” and “long” studies, respectively. In long studies, measurements could be taken up to 6 g of tumor weight, whereas in short studies the experiment was stopped 3 days after the end of treatment. Predicted relative standard errors were smaller in long studies than in corresponding short studies. Some optimal measurement times were located in the regrowth phase, highlighting the importance of continuing the experiment after the end of treatment. In the four-arm designs, the results showed that the proportions of control and treated mice can differ. To conclude, making measurements during tumor regrowth should become a general rule for informative preclinical studies in oncology, especially when a delayed drug effect is suspected.


Fisher information matrix oncology optimal design pharmacodynamic tumor growth inhibition models 



The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 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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Copyright information

© American Association of Pharmaceutical Scientists 2016

Authors and Affiliations

  • Giulia Lestini
    • 1
    • 2
    • 3
    Email author
  • France Mentré
    • 1
    • 2
  • Paolo Magni
    • 3
  1. 1.INSERM, IAME, UMR 1137ParisFrance
  2. 2.Université Paris Diderot, IAME, UMR 1137ParisFrance
  3. 3.Dipartimento di Ingegneria Industriale e dell’InformazioneUniversità degli Studi di PaviaPaviaItaly

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