药物开发
精确肿瘤学
医学
临床前试验
临床前研究
透视图(图形)
药物发现
预测能力
临床肿瘤学
预测值
肿瘤科
计算机科学
计算生物学
医学物理学
药品
生物信息学
内科学
药理学
癌症
生物
人工智能
哲学
认识论
作者
Stephen E. Gould,Melissa R. Junttila,Frédéric J. de Sauvage
出处
期刊:Nature Medicine
[Springer Nature]
日期:2015-05-01
卷期号:21 (5): 431-439
被引量:254
摘要
Much has been written about the advantages and disadvantages of various oncology model systems, with the overall finding that these models lack the predictive power required to translate preclinical efficacy into clinical activity. Despite assertions that some preclinical model systems are superior to others, no single model can suffice to inform preclinical target validation and molecule selection. This perspective provides a balanced albeit critical view of these claims of superiority and outlines a framework for the proper use of existing preclinical models for drug testing and discovery. We also highlight gaps in oncology mouse models and discuss general and pervasive model-independent shortcomings in preclinical oncology work, and we propose ways to address these issues.
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