Designing Dose–Response Studies with Desired Characteristics

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Quantitative Decisions in Drug Development

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

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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 Chapter 6 for assessing a dose–response study design, for both continuous and binary endpoints. We include an example of dose–response studies for an investigational medicinal product to illustrate the importance of covering an adequate dose range in dose-ranging studies.

The dose makes the poison.

—Paracelsus

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References

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

    MATH  Google Scholar 

  • Atkinson, A. C., Donev, A. N., & Tobias, R. D. (2007). Optimum experimental designs, with SAS. 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). MCPMod: An R Package for the design and analysis of dose-finding Studies. Journal of Statistical Software, 29(7), published in February.

    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  Google Scholar 

  • Cross, J., Lee, H., Westelinck, A., et al. (2002). Postmarketing drug dosage changes of 499 FDA-approved new molecular entities, 1980–99. Pharmacoepidemiology and Drug Safety, 11(6), 439–446.

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  • Felson, D. T., Anderson, J. J., Boers, M., et al. (1995). American College of Rheumatology preliminary definition of improvement in rheumatoid arthritis. Arthritis and Rheumatism, 38(6), 727–735.

    Article  Google Scholar 

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

    Book  Google Scholar 

  • ICH E4 (1994). Dose-response information to support drug registration.

    Google Scholar 

  • 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. Available at 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. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. URL 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. Package clinDR. Available at https://cran.r-project.org/web/packages/clinDR/index.html

  • 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 

  • Thomas, N., Roy, D., Somayaji, V., & Sweeney, K. (2014b). Meta-analyses of clinical dose response. Presentation at the European Medicines Agency/European Federation of Pharmaceutical Industries and Associations workshop on the importance of dose finding and dose selection for the successful development, licensing and lifecycle management of medicinal products. Available at http://www.ema.europa.eu/docs/en_GB/document_library/Presentation/2015/01/WC500179795.pdf (accessed 15 February 2021).

  • Turner, H., & Firth, D. (2020) Generalized nonlinear models in R: An overview of the gnm package. Available at https://cran.r-project.org/web/packages/gnm/vignettes/gnmOverview.pdf

  • Wu, J., Banerjee, A., **, B., et al. (2018). Clinical dose-response for a broad set of biological products: A model-based meta-analysis. Statistical Methods in Medical Research, 27(9), 2694–2721.

    Article  MathSciNet  Google Scholar 

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Correspondence to Christy Chuang-Stein .

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Chuang-Stein, C., Kirby, S. (2021). Designing Dose–Response Studies with Desired Characteristics. In: Quantitative Decisions in Drug Development. Springer Series in Pharmaceutical Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-79731-7_8

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