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Nonclinical Statistics for Pharmaceutical and Biotechnology Industries pp 53–81Cite as

Statistical Methods for Drug Discovery

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Part of the book series: Statistics for Biology and Health ((SBH))

Abstract

This chapter is a broad overview of the drug discovery process and areas where statistical input can have a key impact. The focus is primarily in a few key areas: target discovery, compound screening/optimization, and the characterization of important properties. Special attention is paid to working with assay data and phenotypic screens. A discussion of important skills for a nonclinical statistician supporting drug discovery concludes the chapter.

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Notes

  1. 1.

    http://www.pngu.mgh.harvard.edu/purcell/plink/.

  2. 2.

    The term “Mendelian Randomization” refers to the notion that we are randomized at birth to the genetic “treatment” of the SNP.

  3. 3.

    Thankfully, the academic community has been highly co-operative with one another in creating large consortia to produce meta-analyses from many smaller GWAS studies that total to hundreds of thousands of subjects.

  4. 4.

    http://www.iconplc.com.

  5. 5.

    http://www.certara.com.

  6. 6.

    http://www.simcyp.com/.

  7. 7.

    http://www.simulations-plus.com/.

  8. 8.

    http://www.mbswonline.com.

  9. 9.

    http://bit.ly/1qilzvh.

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Acknowledgements

We would like to thank David Potter and Bill Pikounis for providing feedback on a draft of this chapter.

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Authors and Affiliations

  1. Pfizer Global R&D, Groton, CT, USA

    Max Kuhn, Phillip Yates & Craig Hyde

  2. Pfizer’s BioTherapeutics Statistics Group, Groton, CT, USA

    Phillip Yates

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  1. Max Kuhn
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  2. Phillip Yates
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  3. Craig Hyde
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Corresponding author

Correspondence to Max Kuhn .

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Editors and Affiliations

  1. Nonclinical Statistics, Abbvie Inc, North Chicago, Illinois, USA

    Lanju Zhang

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Kuhn, M., Yates, P., Hyde, C. (2016). Statistical Methods for Drug Discovery. In: Zhang, L. (eds) Nonclinical Statistics for Pharmaceutical and Biotechnology Industries. Statistics for Biology and Health. Springer, Cham. https://doi.org/10.1007/978-3-319-23558-5_4

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