Multilevel models improve precision and speed of IC<sub>50</sub> estimates

Journal article (2016)

Authors

Daniel J. Vis Netherlands Cancer Institute
L.F.A. Wessels Pattern Recognition and Bioinformatics - EEMCS
Research Group
Pattern Recognition and Bioinformatics (EEMCS) (TU Delft)
Nonlinear IC50 Dose–response Mixed effects
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Published Date

2016

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

Aim: Experimental variation in dose–response data of drugs tested on cell lines result in inaccuracies in the estimate of a key drug sensitivity characteristic: the IC50. We aim to improve the precision of the half-limiting dose (IC50) estimates by simultaneously employing all dose–responses across all cell lines and drugs, rather than using a single drug–cell line response.
Materials & methods: We propose a multilevel mixed effects model that takes advantage of all available dose–response data.
Results: The new estimates are highly concordant with the currently used Bayesian model when the data are well behaved. Otherwise, the multilevel model is clearly superior.
Conclusion: The multilevel model yields a significant reduction of extreme IC50 estimates, an increase in precision and it runs orders of magnitude faster.