Using the mouse to model human disease: increasing validity and reproducibility
Posted by Disease Models and Mechanisms, on 3 February 2016
ABSTRACT
Experiments that use the mouse as a model for disease have recently come under scrutiny because of the repeated failure of data, particularly derived from preclinical studies, to be replicated or translated to humans. The usefulness of mouse models has been questioned because of irreproducibility and poor recapitulation of human conditions. Newer studies, however, point to bias in reporting results and improper data analysis as key factors that limit reproducibility and validity of preclinical mouse research. Inaccurate and incomplete descriptions of experimental conditions also contribute. Here, we provide guidance on best practice in mouse experimentation, focusing on appropriate selection and validation of the model, sources of variation and their influence on phenotypic outcomes, minimum requirements for control sets, and the importance of rigorous statistics. Our goal is to raise the standards in mouse disease modeling to enhance reproducibility, reliability and clinical translation of findings.
Model validity: making sure the mouse is right
It seems an obvious point, but the model used should be appropriate for the question being addressed. An ideal disease model accurately mimics the human condition, genetically, experimentally and/or physiologically. At DMM, we require that the similarities to human disease be rigorously validated, preferably by proof-of-principle experiments demonstrating response to treatment. The controversial study described above compared microarray gene expression data from humans and mice. In one example, data from human blunt-trauma patients were analyzed together with data from a mouse inbred strain that had been exsanguinated. Losing a large amount of blood does not equate to blunt trauma, and so this could be perceived as comparing apples to oranges. Furthermore, inbred mouse strains represent limited genetic diversity and might not reflect the responses generated in a genetically polymorphic human population. The conclusions drawn in this manuscript did not take into account these potential sources of experimental differences between the mouse and human, and raise the possibility of bias in data analysis.
In a different study, a mouse model was reported to display the key motor symptoms seen in humans with amyotrophic lateral sclerosis (ALS) (Wegorzewska et al., 2009). On the basis of this, the model was used in preclinical studies and promising drug candidates were tested in clinical trials; however, these drugs ultimately failed in humans (Perrin, 2014). It was then shown that this mouse is a poor genetic and phenotypic model of the human condition (Hatzipetros et al., 2014). This example illustrates how relevance to the human disease being studied, supported by strong data to validate the use of the model, is crucial for clinical translation. Humanized models – mice expressing human transgenes or engrafted with functional human cells or tissues – can provide important tools to bridge the gap between animals and humans in preclinical research.
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