稳健性(进化)
抗癌药物
鉴定(生物学)
癌症
药物发现
透视图(图形)
表型筛选
生物
表型
生物信息学
药品
计算机科学
医学
计算生物学
药理学
遗传学
人工智能
基因
植物
出处
期刊:Nature Reviews Cancer
[Springer Nature]
日期:2017-05-19
卷期号:17 (7): 441-450
被引量:152
摘要
An alarming number of papers from laboratories nominating new cancer drug targets contain findings that cannot be reproduced by others or are simply not robust enough to justify drug discovery efforts. This problem probably has many causes, including an underappreciation of the danger of being misled by off-target effects when using pharmacological or genetic perturbants in complex biological assays. This danger is particularly acute when, as is often the case in cancer pharmacology, the biological phenotype being measured is a 'down' readout (such as decreased proliferation, decreased viability or decreased tumour growth) that could simply reflect a nonspecific loss of cellular fitness. These problems are compounded by multiple hypothesis testing, such as when candidate targets emerge from high-throughput screens that interrogate multiple targets in parallel, and by a publication and promotion system that preferentially rewards positive findings. In this Perspective, I outline some of the common pitfalls in preclinical cancer target identification and some potential approaches to mitigate them.
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