Improving the predictive value of interventional animal models data.
Improving the predictive value of interventional animal models data.
复制标题
提高介入动物模型数据的预测价值。
DOI：
10.1016/j.drudis.2014.10.015
复制
发表时间：
2015
影响因子：
7.4
通讯作者：
Zeiss,CarolineJ
中科院分区：
文献类型：
--
作者：
Zeiss,CarolineJ
对752项阿尔茨海默病的人类和动物研究中的448项干预措施进行了比较。基于网络的系统生物学方法被用来评估干预措施和机制的跨物种结果的可重复性。这种方法支持在计算机上减少动物研究中的积极结果偏倚。对于许多慢性疾病，使用动物模型范式的转化成功已经陷入僵局。以阿尔茨海默病为例，本文采用基于网络的方法来评估跨物种的结果的可重复性，通过干预和机制。超过75%的动物研究报告了改善的结果。菌株背景是一个重要的潜在混淆因素。5%的干预措施已经在动物和人类身上进行了测试，或者在三个或更多的动物模型上进行了测试。多奈哌齐、美金刚和运动在不同物种中产生了积极的结果。淀粉样蛋白假说和三种其他机制在动物中确定了可重复的阳性结果。这种方法支持在动物研究中通过计算机模拟减少阳性结果偏倚。
Highlights448 interventions across 752 human and animal studies for Alzheimer's disease were compared.A networks-based systems biology approach was used to assess repeatability of outcomes across species by intervention and mechanism.This approach supports in silico reduction of positive outcomes bias in animal studies.For many chronic diseases, translational success using the animal model paradigm has reached an impasse. Using Alzheimer's disease as an example, this review employs a networks-based method to assess repeatability of outcomes across species, by intervention and mechanism. Over 75% of animal studies reported an improved outcome. Strain background was a significant potential confounder. Five percent of interventions had been tested across animals and humans, or examined across three or more animal models. Positive outcomes across species emerged for donepezil, memantine and exercise. Repeatable positive outcomes in animals were identified for the amyloid hypothesis and three additional mechanisms. This approach supports in silico reduction of positive outcomes bias in animal studies.