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

Research Article

Abstract

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.

KEY WORDS

Fisher information matrix oncology optimal design pharmacodynamic tumor growth inhibition models 

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    Magni P, Bertoldo A, Vicini P. 7 - Population modelling. In: Cobelli EC, editor. Modelling methodology for physiology and medicine (second edition) [Internet]. Oxford: Elsevier; 2014 [cited 2015 Nov 17]. p. 131–58. Available from: http://www.sciencedirect.com/science/article/pii/B9780124115576000070.
  11. 11.
    Lavielle M. Mixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools. Chapman and Hall/CRC; 2014. 383 p. (Biostatistics Series).Google Scholar
  12. 12.
    Lalonde RL, Kowalski KG, Hutmacher MM, et al. Model-based drug development. Clin Pharmacol Ther. 2007;82(1):21–32.CrossRefPubMedGoogle Scholar
  13. 13.
    Smith BP, Vincent J. Biostatistics and pharmacometrics: quantitative sciences to propel drug development forward. Clin Pharmacol Ther. 2010;88(2):141–4.CrossRefPubMedGoogle Scholar
  14. 14.
    al-Banna MK, Kelman AW, Whiting B. Experimental design and efficient parameter estimation in population pharmacokinetics. J Pharmacokinet Biopharm. 1990;18(4):347–60.CrossRefPubMedGoogle Scholar
  15. 15.
    Holford N, Ma SC, Ploeger BA. Clinical trial simulation: a review. Clin Pharmacol Ther. 2010;88(2):166–82.CrossRefPubMedGoogle Scholar
  16. 16.
    Mentré F, Mallet A, Baccar D. Optimal design in random-effects regression models. Biometrika. 1997;84(2):429–42.CrossRefGoogle Scholar
  17. 17.
    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
  18. 18.
    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
  19. 19.
    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 [Internet]. Available from: (http://www.page-meeting.org/default.asp?abstract=3032).
  20. 20.
    Bazzoli C, Retout S, Mentré F. Design evaluation and optimisation in multiple response nonlinear mixed effect models: PFIM 3.0. Comput Methods Prog Biomed. 2010;98(1):55–65.CrossRefGoogle Scholar
  21. 21.
    Nyberg J, Ueckert S, Strömberg EA, Hennig S, Karlsson MO, Hooker AC. PopED: an extended, parallelized, nonlinear mixed effects models optimal design tool. Comput Methods Prog Biomed. 2012;108(2):789–805.CrossRefGoogle Scholar
  22. 22.
    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
  23. 23.
    Duffull SB. POPT - Installation and user guide. University of Otago. 2006.Google Scholar
  24. 24.
    Atkinson A, Donev A, Tobias R. Optimum experimental designs, with SAS. 2007. (Oxford Statistical Science Series).Google Scholar
  25. 25.
    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
  26. 26.
    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
  27. 27.
    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
  28. 28.
    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).
  29. 29.
  30. 30.
    Hather G, Liu R, Bandi S, et al. Growth rate analysis and efficient experimental design for tumor xenograft studies. Cancer Informat. 2014;13 Suppl 4:65–72.CrossRefGoogle Scholar
  31. 31.
    Tumor Policy for Mice and Rats » Research Committees » Boston University [Internet]. [cited 2016 Apr 4]. Available from: http://www.bu.edu/orccommittees/iacuc/policies-and-guidelines/tumor-policy-for-mice-and-rats/.
  32. 32.
    Dodds MG, Hooker AC, Vicini P. Robust population pharmacokinetic experiment design. J Pharmacokinet Pharmacodyn. 2005;32(1):33–64.CrossRefPubMedGoogle Scholar
  33. 33.
    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
  34. 34.
    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
  35. 35.
    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
  36. 36.
    Hoeting J, Madigan D, Raftery A, Volinsky C. Bayesian model averaging: a tutorial. Stat Sci. 1999;14(4):382–417.CrossRefGoogle Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2016

Authors and Affiliations

  • Giulia Lestini
    • 1
    • 2
    • 3
  • 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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