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Key Aspects of Modern, Quantitative Drug Development

Author

Listed:
  • Eric Gibson

    (Novartis Pharmaceuticals)

  • Frank Bretz

    (Novartis Pharma AG
    Medical University of Vienna)

  • Michael Looby

    (Novartis Pharma AG)

  • Bjoern Bornkamp

    (Novartis Pharma AG)

Abstract

One of the main goals of modern drug development is customized care, where doctors match the right patient to the right treatment at the right dose, based on quantitative evidence. In this paper we review three key aspects of drug development that are critical towards achieving this goal. More specifically, we discuss (i) the advantages of modern model-based dose-finding as opposed to traditional pairwise comparisons, (ii) the value of pharmacometrical modeling, understanding the variability in how patients metabolize, tolerate, and respond to drugs, and (iii) the potential impact of enrichment strategies to identify study populations that are most likely to benefit from the investigational drug under development.

Suggested Citation

  • Eric Gibson & Frank Bretz & Michael Looby & Bjoern Bornkamp, 2018. "Key Aspects of Modern, Quantitative Drug Development," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(2), pages 283-296, August.
    • Handle: RePEc:spr:stabio:v:10:y:2018:i:2:d:10.1007_s12561-017-9203-2
    DOI: 10.1007/s12561-017-9203-2
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    References listed on IDEAS

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    1. Chris Chatfield, 1995. "Model Uncertainty, Data Mining and Statistical Inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(3), pages 419-444, May.
    2. F. Bretz & J. C. Pinheiro & M. Branson, 2005. "Combining Multiple Comparisons and Modeling Techniques in Dose-Response Studies," Biometrics, The International Biometric Society, vol. 61(3), pages 738-748, September.
    3. Dette, Holger & Bretz, Frank & Pepelyshev, Andrey & Pinheiro, José, 2008. "Optimal Designs for Dose-Finding Studies," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1225-1237.
    4. Bornkamp, Björn & Pinheiro, José & Bretz, Frank, 2009. "MCPMod: An R Package for the Design and Analysis of Dose-Finding Studies," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 29(i07).
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