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ISSN: 2153-0645

Journal of Pharmacogenomics & Pharmacoproteomics
Open Access

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Review Article

Beyond IC50s: Towards Robust Statistical Methods for in vitro Association Studies

Andrew Beam1 and Alison Motsinger-Reif1,2*

1 Bioinformatics Research Center, North Carolina State University, Raleigh NC, USA

2 Department of Statistics, North Carolina State University, Raleigh NC, USA

*Corresponding Author:
Alison A. Motsinger-Reif
Bioinformatics Research Center, 1 Lampe Drive
CB 7566, Ricks Hall, Raleigh, NC 27695, USA
Tel: 919-515-3574
E-mail: [email protected]

Received date: November 14, 2013; Accepted date: December 02, 2013; Published date: December 10, 2013

Citation: Beam A, Motsinger-Reif1 A (2013) Beyond IC50s: Towards Robust Statistical Methods for in vitro Association Studies. J Pharmacogenomics Pharmacoproteomics 5:121. doi: 10.4172/2153-0645.1000121

Copyright: © 2013 Beam A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Cell line cytotoxicity assays have become increasingly popular approaches for genetic and genomic studies of differential cytotoxic response. There are an increasing number of success stories, but relatively little evaluation of the statistical approaches used in such studies. In the vast majority of these studies, concentration response is summarized using curve-fitting approaches, and then summary measure(s) are used as the phenotype in subsequent genetic association studies. The curve is usually summarized by a single parameter such as the curve’s inflection point (e.g. the EC/IC50). Such modeling makes major assumptions and has statistical limitations that should be considered. In the current review, we discuss the limitations of the EC/IC50 as a phenotype in association studies, and highlight some potential limitations with a simulation experiment. Finally, we discuss some alternative analysis approaches that have been shown to be more robust.

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