Neuroscience research is among the single largest animal research fields, with around 1.3 million animals used each year in the EU alone. In Switzerland, more than a quarter of experimental animal used in recent years were in neuroscience, and many undergoing experimental procedures of high severity. Despite the use of these numerous experimental animals, the overall success rate in therapy development for neurological diseases such as Alzheimer’s dementia and stroke is low compared to other fields. Bridging this translational gap is critical to advancing both science and the 3Rs of replacement, reduction, and refinement. Although there are different reasons for this gap, weak or inappropriate design of preclinical studies has been flagged as key driver. For instance, the selection of animal disease models is often made based on the resources available to that researcher rather than which model provides the best representation of the specific pathophysiological process under study. Yet there is a lack of a comprehensive resource to assist preclinical neuroscientists make informed decisions on experimental parameters during study planning. This costs many experimental animals with only modest translational relevance. The advent of complex cell culture models, such as 3D organoids or microphysiological systems, has brought prospects to partly replace animal experiments for drug testing. However, no systematic curation of the application of such models has demonstrated their translational value in comparison with respective animal models.
Our ambition is to systematically curate the neurological-psychiatric therapy development pipeline, including the use of animal and 3D culture models, by harnessing artificial intelligence and text analysis techniques. With this, our goal is to provide a comprehensive, up-to-date overview of translational success rates in neuroscience, including details of the experimental approaches used (such as animal models, therapies, candidate therapy classes and study quality) and to benchmark the performance of 3D culture models against respective animal models. Statistical modelling will be employed to identify potential experimental parameters being associated with successful translation. This evidence-based resource will be made available as “living” online data warehouse, named ANIMONE (ANImal MOdels in NEuroscience), to guide researchers in designing their own animal studies.
This biocuration, with clear transparency of the provenance of the information provided, will foster evidence-based practice among preclinical neuroscientists. With this, it will support the reduction and refinement of animal studies in neuroscience. Benchmarking animal models against 3D culture models can promote replacement of animal experiments. But our findings and developed resources can benefit additional animal welfare measures: if animal research is conducted to higher standards, through improved rigour and reporting, this enhances the ethical harm–benefit calculus by improving benefit. Furthermore, this resource and the methodology developed will encourage preclinical systematic reviews—a recognised approach to improving animal welfare. Finally, a richer understanding of translational success rates will foster a more informed discourse on the role of animal experiments among stakeholders in animal research and in the public sphere.
Details of the project, please find: here
The Ineichen Lab is currently looking for: Master students to join the team.
Inquiries can be sent to Benjamin Ineichen: firstname.lastname@example.org