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Designing Dose-Response Studies with Desired Characteristics

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Part of the book series: Springer Series in Pharmaceutical Statistics ((SSPS))

Abstract

According to ICH-E4, “Elucidation of the dose-response function” is a key stage in drug development. Consequently, designing a dose-response study with the desired characteristics is an important activity in drug development. Inadequate dose-response knowledge has been known to lead to a delay or denial in regulatory approvals of initial drug applications. There have also been cases when the dose initially approved for a marketed product had to be reduced subsequently. In this chapter we focus on using the Emax model to describe a dose-response relationship, but the discussion applies equally to other dose-response models or to a collection of models. We examine in detail the three metrics introduced in Chap. 6 for assessing a dose-response study design.

The dose makes the poison.

Paracelsus

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References

  • Atkinson, A. C., & Donev, A. N. (1992). Optimum experimental designs. Oxford: Clarendon Press.

    MATH  Google Scholar 

  • Atkinson, A. C., Donev, A. N., & Tobias, R. D. (2007). Optimum experimental designs, with SAS. Oxford: Oxford University Press.

    MATH  Google Scholar 

  • Bornkamp, B., Bretz, F., Dmitrienko, A., et al. (2007). Innovative approaches for designing and analyzing adaptive dose-ranging trials. Journal of Biopharmaceutical Statistics, 17(6), 965–995.

    Article  MathSciNet  Google Scholar 

  • Bornkamp, B., Pinheiro, J., & Bretz, F. (2009, February). MCPMod: An R package for the design and analysis of dose-finding studies. Journal of Statistical Software, 29(7).

    Google Scholar 

  • Brain, P., Kirby, S., & Larionov, R. (2014). Fitting Emax models to clinical trial dose–response data when the high dose asymptote is ill defined. Pharmaceutical Statistics, 13(6), 364–370.

    Article  Google Scholar 

  • Bretz, F., Pinheiro, J. C., & Branson, M. (2005). Combining multiple comparisons and modeling techniques in dose–response studies. Biometrics, 61(3), 738–748.

    Article  MathSciNet  MATH  Google Scholar 

  • Cross, J., Lee, H., Westelinck, A., et al. (2002). Postmarketing drug dosage changes of 499 FDA-approved new molecular entities, 1980–1999. Pharmacoepidemiology and Drug Safety, 11(6), 439–446.

    Article  Google Scholar 

  • Dette, H., Kiss, C., Bevanda, M., & Bretz, F. (2010). Optimal designs for Emax, log-linear and exponential models. Biometrika, 97(2), 513–518.

    Article  MathSciNet  MATH  Google Scholar 

  • Fedorov, V. V., & Leonov, S. L. (2013). Optimal design for nonlinear response models. Boca Raton, FL: Chapman Hall/CRC Press.

    MATH  Google Scholar 

  • ICH. (1994). ICH-E4 dose–response information to support drug registration. Retrieved June 22, 2016, from http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E4/Step4/E4_Guideline.pdf

  • Kirby, S., Colman, P., & Morris, M. (2009). Adaptive modelling of dose–response relationships using smoothing splines. Pharmaceutical Statistics, 8(4), 346–355.

    Google Scholar 

  • Masoudi, E., Sarmad, M., & Talebi, H. (2013). Package LDOD. https://cran.r-project.org/web/packages/LDOD/index.html

  • Pinheiro, J., Sax, F., Antonijevic, Z., et al. (2010). Adaptive and model-based dose-ranging trials: Quantitative evaluation and recommendations. Statistics in Biopharmaceutical Research, 2(4), 435–454.

    Article  Google Scholar 

  • R Core Team. (2014). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. isbn:3-900051-07-0, http://www.R-project.org/

  • Sacks, L. V., Shamsuddin, H. H., Yasinskaya, Y. I., et al. (2014). Scientific and regulatory reasons for delay and denial of FDA approval of initial applications for new drugs, 2000–2012. Journal of the American Medical Association, 311(4), 378–384.

    Article  Google Scholar 

  • Smith, M. K., Jones, I., Morris, M. F., et al. (2006). Implementation of a Bayesian adaptive design in a proof of concept study. Pharmaceutical Statistics, 5(1), 39–50.

    Article  Google Scholar 

  • Thomas, N., Sweeney, K., & Somayaji, V. (2014). Meta-analysis of clinical dose–response in a large drug development portfolio. Statistics in Biopharmaceutical Research, 6(4), 302–317.

    Article  Google Scholar 

  • Turner, H., & Firth, D. (2015). Generalized nonlinear models in R: An overview of the gnm package. https://cran.r-project.org/web/packages/gnm/vignettes/gnmOverview.pdf

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Chuang-Stein, C., Kirby, S. (2017). Designing Dose-Response Studies with Desired Characteristics. In: Quantitative Decisions in Drug Development. Springer Series in Pharmaceutical Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-46076-5_8

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