Is it possible to express in numbers the magnitude of the damage caused by the translation problem?
In preclinical stroke research, it is estimated that less than half of the apparently promising results published are truly positive. The extent of the translation problem varies. But the majority of therapeutics identified as promising in preclinical research fail in subsequent clinical trials. The magnitude of the problem cannot be expressed solely in terms of losses in time or money. Above all, there are ethical consequences too. People are exposing themselves to therapies that may be useless or even damaging.
What way out do you think there is?
My own experiences in research prompted me to get behind the causes of poor translation. During my time at the University of Edinburgh, I joined the "CAMARADES" group (Collaborative Approach to Meta-Analysis and Review of Animal Data from Experimental Studies). Colleagues there aim to improve preclinical research by pooling published data in the form of systematic reviews and meta-analyses. For more than three years now, I have been working at the QUEST Center of the BIH, where I have also established a CAMARADES group. The COReS project will help us to establish the concept on a larger scale: The title stands for "Communities for Open Research Synthesis - accelerating translation of biomedical evidence".
Why might systematic reviews be a way out?
A systematic review compiles all the study data available and thus provides an overview of the knowledge in a defined area of research and of the data quality. It makes it possible to discover gaps in knowledge. Such a complete picture of the state of research facilitates the planning of further research. The systematic review not only increases the chances that the most promising therapies, i.e., those with the most robust evidence, will actually advance into clinical research. It also avoids duplicative and unnecessary experiments. This also reduces the number of animal experiments.
Ideally, we will reach out to everyone involved in biomedical research and motivate them to participate.
Who in the community do you want to target with COReS?
Ideally, we will reach out to everyone involved in biomedical research and motivate them to participate. First of all, the most important people are those working in the laboratory and actually producing the data.