Improved design of preclinical animal studies by matching tools – a key for effective clinical translation
Poor reproducibility of pre-clinical animal studies and the general lack of translatability of the findings are major obstacles hindering the drug development process. According to Professor Tero Aittokallio and his colleagues, the situation could be improved by statistical animal matching tools.
In their opinion paper, published today in Science Translational Medicine, Tero Aittokallio, Andreas Scherer, Matti Poutanen and Leonard P. Freedman argue that the better use of baseline stratification factors in the preclinical animal studies should improve the quality of the results and, in due course, the success of human trials and clinical applications. The authors promote the wide use of tailored statistical approaches and accompanying software solutions to help researchers to implement existing animal experiment guidelines and improved practices.
The application of matched preclinical designs in animal studies is significant because it confers greater statistical power to detect true treatment effects without the higher costs and ethical considerations of using more animals in a given study. This in turn is impactful as it will lead to both improved reproducibility of preclinical work as well as to translation to the potential success of human studies.
- Leonard P. Freedman, President of Global Biological Standards Institute.
Aittokallio’s group has recently published an algorithm that enables matching of the animals based on all available baseline variables and thus assisting researchers in optimizing the study setting and minimizing the number of animals needed to achieve statistically significant results. The tool is freely available through a web-based user interface and the group invites the research community to test and utilize it.
There is a critical need for a larger community effort towards pre-clinical research reproducibility, both when using in vitro and in vivo models. In animal studies, the goal is to supplement the current ARRIVE guidelines with additional standards for improved statistical design and analysis of treatment experiments.
- FIMM-EMBL Group Leader Tero Aittokallio
To promote wide implementation of better practices, international efforts are needed to spread the word about the already existing matching tools and to train researchers to use them.
The European Infrastructure for Translational Medicine (EATRIS) will take an active role in such activities, for which it is well-suited thanks to its large network of more than 85 high-quality research centers in 11 member states in Europe, and its large number of links to other international organizations acting the biomedical field..
- Andreas Scherer, National Coordinator for EATRIS in Finland, who is heading the data quality initiative for EATRIS at the international level