药物发现
药品
计算生物学
灵敏度(控制系统)
生长抑制
生物
生物标志物发现
细胞生长
药理学
生物信息学
遗传学
基因
蛋白质组学
电子工程
工程类
作者
Marc Hafner,Mario Niepel,Mirra Chung,Peter K. Sorger
出处
期刊:Nature Methods
[Springer Nature]
日期:2016-05-02
卷期号:13 (6): 521-527
被引量:559
摘要
Growth inhibition metrics allow for robust measurement of drug efficacy independent of variables such as cell growth rate, seeding density, and growth medium; they are a practical alternative to metrics such as IC50 and offer enhanced reproducibility. Drug sensitivity and resistance are conventionally quantified by IC50 or Emax values, but these metrics are highly sensitive to the number of divisions taking place over the course of a response assay. The dependency of IC50 and Emax on division rate creates artefactual correlations between genotype and drug sensitivity, while obscuring valuable biological insights and interfering with biomarker discovery. We derive alternative small molecule drug-response metrics that are insensitive to division number. These are based on estimation of the magnitude of drug-induced growth rate inhibition (GR) using endpoint or time-course assays. We show that GR50 and GRmax are superior to conventional metrics for assessing the effects of small molecule drugs in dividing cells. Moreover, adopting GR metrics requires only modest changes in experimental protocols. We expect GR metrics to improve the study of cell signaling and growth using small molecules and biologics and to facilitate the discovery of drug-response biomarkers and the identification of drugs effective against specific patient-derived tumor cells.
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