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.
This is a preview of subscription content, log in to check access.
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.
Bernard A, Kimko H, Mital D, Poggesi I. Mathematical modeling of tumor growth and tumor growth inhibition in oncology drug development. Expert Opin Drug Metab Toxicol. 2012;8(9):1057–69.CrossRefPubMedGoogle Scholar
Kelland LR. Of mice and men: values and liabilities of the athymic nude mouse model in anticancer drug development. Eur J Cancer Oxf Engl 1990. 2004;40(6):827–36.Google Scholar
Mattern J, Bak M, Hahn EW, Volm M. Human tumor xenografts as model for drug testing. Cancer Metastasis Rev. 1988;7(3):263–84.CrossRefPubMedGoogle Scholar
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.CrossRefPubMedGoogle Scholar
Bissery MC, Vrignaud P, Lavelle F, Chabot GG. Experimental antitumor activity and pharmacokinetics of the camptothecin analog irinotecan (CPT-11) in mice. Anticancer Drugs. 1996;7(4):437–60.CrossRefPubMedGoogle Scholar
Rocchetti M, Poggesi I, Germani M, et al. A pharmacokinetic-pharmacodynamic model for predicting tumour growth inhibition in mice: a useful tool in oncology drug development. Basic Clin Pharmacol Toxicol. 2005;96(3):265–8.CrossRefPubMedGoogle Scholar
Simeoni M, Magni P, Cammia C, 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.CrossRefPubMedGoogle Scholar
Magni P, Simeoni M, Poggesi I, Rocchetti M, De Nicolao G. A mathematical model to study the effects of drugs administration on tumor growth dynamics. Math Biosci. 2006;200(2):127–51.CrossRefPubMedGoogle Scholar
Terranova N, Germani M, Del Bene F, Magni P. A predictive pharmacokinetic–pharmacodynamic model of tumor growth kinetics in xenograft mice after administration of anticancer agents given in combination. Cancer Chemother Pharmacol. 2013;72(2):471–82.CrossRefPubMedPubMedCentralGoogle Scholar
Mentré F, Mallet A, Baccar D. Optimal design in random-effects regression models. Biometrika. 1997;84(2):429–42.CrossRefGoogle Scholar
Nyberg J, Bazzoli C, Ogungbenro K, et al. Methods and software tools for design evaluation in population pharmacokinetics–pharmacodynamics studies. Br J Clin Pharmacol. 2015;79(1):6–17.CrossRefPubMedGoogle Scholar
Mentré F, Chenel M, Comets E, et al. Current use and developments needed for optimal design in pharmacometrics: a study performed among DDMoRe’s European Federation of Pharmaceutical Industries and Associations Members. CPT Pharmacometrics Syst Pharmacol. 2013;2(6), e46.CrossRefPubMedPubMedCentralGoogle Scholar
Gueorguieva I, Ogungbenro K, Graham G, Glatt S, Aarons L. A program for individual and population optimal design for univariate and multivariate response pharmacokinetic–pharmacodynamic models. Comput Methods Prog Biomed. 2007;86(1):51–61.CrossRefGoogle Scholar
Duffull SB. POPT - Installation and user guide. University of Otago. 2006.Google Scholar
Atkinson A, Donev A, Tobias R. Optimum experimental designs, with SAS. 2007. (Oxford Statistical Science Series).Google Scholar
Nagy Z, Baghy K, Hunyadi-Gulyás É, et al. Evaluation of 9-cis retinoic acid and mitotane as antitumoral agents in an adrenocortical xenograft model. Am J Cancer Res. 2015;5(12):3645–58.PubMedPubMedCentralGoogle Scholar
Chang L, Gong F, Cai H, Li Z, Cui Y. Combined RNAi targeting human Stat3 and ADAM9 as gene therapy for non-small cell lung cancer. Oncol Lett. 2016;11(2):1242–50.PubMedGoogle Scholar
Rocchetti M, Germani M, Del Bene F, et al. Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth after administration of an anti-angiogenic agent, bevacizumab, as single-agent and combination therapy in tumor xenografts. Cancer Chemother Pharmacol. 2013;71(5):1147–57.CrossRefPubMedGoogle Scholar
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 503 [Internet]. Available from: (www.page-meeting.org/?abstract=503).
Dodds MG, Hooker AC, Vicini P. Robust population pharmacokinetic experiment design. J Pharmacokinet Pharmacodyn. 2005;32(1):33–64.CrossRefPubMedGoogle Scholar
Tod M, Rocchisani JM. Comparison of ED, EID, and API criteria for the robust optimization of sampling times in pharmacokinetics. J Pharmacokinet Biopharm. 1997;25(4):515–37.CrossRefPubMedGoogle Scholar
Vajjah P, Duffull SB. A generalisation of T-optimality for discriminating between competing models with an application to pharmacokinetic studies. Pharm Stat. 2012;11(6):503–10.CrossRefPubMedGoogle Scholar
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. 2015;32(10):3159–69.CrossRefPubMedGoogle Scholar
Hoeting J, Madigan D, Raftery A, Volinsky C. Bayesian model averaging: a tutorial. Stat Sci. 1999;14(4):382–417.CrossRefGoogle Scholar