Injury of the adult central nervous system of mammals results in lasting deficits including permanent motor and sensory impairments due to lack of profound neuronal regeneration. In particular, patients suffering from spinal cord injury (SCI) remain paralyzed for the rest of their lives and often suffer from additional complications. Preclinical research in the field of central nervous system trauma is advancing at a fast pace and yields over 8,000 new publications per year growing at an exponential rate, yielding a total amount of approximately 160,000 PubMed-listed papers today.
The vast amount of published information exceeds the capacity of the individual scientist to absorb the relevant knowledge by far. Therefore, the knowledge that underlies decision making in selecting the most promising therapeutic interventions for further research or clinical trials is notoriously incomplete. We develop an information extraction workflow for automatically structuring this knowledge and storing it in a database covering all published preclinical experiments on SCI treatment. Being made available to clinical and preclinical researchers, this database will support the selection of the most promising therapeutic setting for clinical trials or animal experiments, help to compare their results to previous experiments with similar settings or specifically altered parameters. Thus, it will enable meta-analyses and systematic comparisons on the basis of all scientific data available in the scientific literature in order to identify aspects of preclinical studies which increase the probability for therapies to be successfully translated across species, with the final goal to translate them into humans.