Optimal design for informative protocols in xenograft tumor growth inhibition models
Giulia Lestini (1,2), France Mentré (1), Paolo Magni (2)
(1) IAME, UMR 1137, INSERM, University Paris Diderot, Paris, France, (2) Dipartimento di Ingegneria Industriale e dell’Informazione, Università degli Studi di Pavia, Pavia, Italy
Objectives: The in vivo evaluation of antitumor effect is an important step of the preclinical drug development. Xenograft experiments are performed, but tumor size measurements are usually taken only during treatment [1], preventing a correct identification of certain parameters of Tumor Growth Inhibition (TGI) models. Our aim was to use optimal design approach in TGI models to evaluate the importance of including measurements during the tumor regrowth phase in those studies.
Methods: We considered the Simeoni TGI model [2]. Optimal design was performed for several examples of xenograft experiments in treated and control arms, reported in [2,3], involving different drugs, schedules and cell lines. Various scenarios were studied. Basic scenarios are those with same real settings as in [2,3]. In other scenarios, the parameter related to the cells death rate (k1), was set larger than the reported value to assess the effect on the experimental design. Finally sampling design was optimized, for each selected experiment, with or without the constraint of not sampling during tumor regrowth, that we defined as “short” and “long” studies, respectively. In the long study, measurements could be taken up to six grams of tumor weight, for ethical reasons, whereas in the short study the experiment was stopped two or three days after the end of the period of treatment. Design optimization was performed using the determinant of the Fisher Information Matrix in PFIM 4.0 [4]. Predicted Relative Standard Errors (RSE) and D-optimal criterion were used to compare those scenarios.
Results: As expected, predicted RSE and D-optimal criterion obtained in long studies were better compared to those obtained in the short study of the corresponding experiments. Indeed, some optimal times were located in the regrowth phase, highlighting the importance of continuing the experiment also after the end of the treatment.
Conclusions: Based on results obtained here, making measurements during tumor regrowth should become a general rule for more informative preclinical studies in oncology.
Acknowledgements: This work was supported by the DDMoRe project (www.ddmore.eu).
References:
[1] Simeoni M, De Nicolao G, Magni P, Rocchetti M, Poggesi I. Modeling of human tumor xenografts and dose rationale in oncology. Drug Discov Today Technol. (2013) 10(3):e365–72.
[2] Simeoni M, Magni P, Cammia C, De Nicolao G, Croci V, Pesenti E, et al. Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth kinetics in xenograft models after administration of anticancer agents. Cancer Res. (2004) 64(3):1094–101.
[3] Simeoni M, Poggesi I, Germani M, De Nicolao G, Rocchetti M. Population modeling of tumor growth inhibition in vivo: application to anticancer drug development. PAGE (2004) Abstr 496. Available from: [www.page-meeting.org/?abstract=496]
[4] Mentré F, Thu Thuy N, Lestini G, Dumont C, PFIM group. PFIM 4.0: new features for optimal design in nonlinear mixed effects models using R. PAGE (2014) Abstr 3032. Available from: [http://www.page-meeting.org/default.asp?abstract=3032]
