The Significance of Meaning: Why Do Over 90% of Behavioral Neuroscience Results Fail to Translate to Humans, and What Can We Do to Fix It?
The Significance of Meaning: Why Do Over 90% of Behavioral Neuroscience Results Fail to Translate to Humans, and What Can We Do to Fix It?
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DOI：
10.1093/ilar/ilu047
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发表时间：
2014-01-01
期刊：
影响因子：
2.5
通讯作者：
Garner, Joseph P.
中科院分区：
文献类型：
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作者：
Garner, Joseph P.
绝大多数进入人体试验的药物都失败了。这个问题（被称为“损耗”）被广泛认为是一场公共卫生危机，并且在过去二十年里一直被公开讨论。近期的多篇综述认为，动物在生理、解剖和心理方面可能与人类差异过大，以至于无法预测人类的结果，这从根本上质疑了动物基础生物医学研究的合理性。然而，这篇综述却认为，基础动物研究和人体临床试验在实验设计和分析的理念与实践上差异巨大，以至于（按照目前的方式进行的）动物实验无法合理地预测人体试验的结果。因此，损耗确实反映了动物实验缺乏预测有效性，但如果得出动物模型无法显示预测有效性的结论，那将是一个悲剧性的错误。本文综述了导致有效性不佳的多种因素。强调需要采用高度特异的方法和模型（即能够识别真阴性结果的方法和模型），以补充目前占主导地位的高度敏感的方法（这些方法容易出现假阳性结果）。提出了基于生物标志物的医学概念作为一种潜在的解决方案，并概述了采用基于生物标志物的转化方法所需的动物模型使用的变化。从本质上讲，这篇综述倡导一种根本性的转变，即我们尽可能将动物实验的各个方面都当作是在人类群体中进行的临床试验来对待。然而，期望研究人员在成功的人体试验之前采用一种无法通过经验证明合理的新方法是不现实的。提出“用已知失败案例进行验证”作为一种解决方案。因此，可以使用一种已转化的药物（已知阳性）和一种失败的药物（已知阴性）将新方法或新模型与现有方法或模型进行比较。目前的方法应该会错误地将两者都识别为有效，但一种更特异的方法应该能正确识别出阴性化合物。通过使用一个已知失败案例库，我们可以据此经验性地测试诸如富集、可控异质化、基于生物标志物的模型或反向转化措施等建议解决方案的影响。
The vast majority of drugs entering human trials fail. This problem (called "attrition") is widely recognized as a public health crisis, and has been discussed openly for the last two decades. Multiple recent reviews argue that animals may be just too different physiologically, anatomically, and psychologically from humans to be able to predict human outcomes, essentially questioning the justification of basic biomedical research in animals.This review argues instead that the philosophy and practice of experimental design and analysis is so different in basic animal work and human clinical trials that an animal experiment (as currently conducted) cannot reasonably predict the outcome of a human trial. Thus, attrition does reflect a lack of predictive validity of animal experiments, but it would be a tragic mistake to conclude that animal models cannot show predictive validity.A variety of contributing factors to poor validity are reviewed. The need to adopt methods and models that are highly specific (i.e., which can identify true negative results) in order to complement the current preponderance of highly sensitive methods (which are prone to false positive results) is emphasized. Concepts in biomarker-based medicine are offered as a potential solution, and changes in the use of animal models required to embrace a translational biomarker- based approach are outlined. In essence, this review advocates a fundamental shift, where we treat every aspect of an animal experiment that we can as if it was a clinical trial in a human population.However, it is unrealistic to expect researchers to adopt a new methodology that cannot be empirically justified until a successful human trial. "Validation with known failures" is proposed as a solution. Thus new methods or models can be compared against existing ones using a drug that has translated (a known positive) and one that has failed (a known negative). Current methods should incorrectly identify both as effective, but a more specific method should identify the negative compound correctly. By using a library of known failures we can thereby empirically test the impact of suggested solutions such as enrichment, controlled heterogenization, biomarker-based models, or reverse-translated measures.