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Compound Ranking Based on a New Mathematical Measure of Effectiveness Using Time Course Data from Cell-Based Assays

[ Vol. 16 , Issue. 3 ]


Francisco J. Diaz, Peter R. McDonald, Anuradha Roy, Byron Taylor, Ashleigh Price, Jessica Hall, Brian S.J. Blagg and Rathnam Chaguturu   Pages 168 - 179 ( 12 )


The half maximal inhibitory concentration (IC50) has several limitations that make it unsuitable for examining a large number of compounds in cytotoxicity studies, particularly when multiple exposure periods are tested. This article proposes a new approach to measure drug effectiveness, which allows ranking compounds according to their toxic effects on live cells. This effectiveness measure, which combines all exposure times tested, compares the growth rates of a particular cell line in the presence of the compound with its growth rate in the presence of DMSO alone. Our approach allows measuring a wider spectrum of toxicity than the IC50 approach, and allows automatic analyses of a large number of compounds. It can be easily implemented in linear regression software, provides a comparable measure of effectiveness for each investigated compound (both toxic and non-toxic), and allows statistically testing the null hypothesis that a compound is non-toxic versus the alternative that it is toxic. Importantly, our approach allows defining an automated decision rule for deciding whether a compound is significantly toxic. As an illustration, we describe the results of a cellbased study of the cytotoxicity of 24 analogs of novobiocin, a C-terminal inhibitor of heat shock protein 90 (Hsp90); the compounds were ranked in order of cytotoxicity to a panel of 18 cancer cell lines and 1 normal cell line. Our approach may also be a good alternative to computing the half maximal effective concentration (EC50) in studies searching for compounds that promote cell growth.


Anticancer drugs, cancer cell lines, cytotoxicity, drug effectiveness, EC50, exponential growth model, high throughput screening, hit selection, IC50


Department of Biostatistics, University of Kansas Medical Center, Mail Stop 1026, 3901 Rainbow Blvd., Kansas City, KS 66160, USA.

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