Evidence-based comparative severity assessment in young and adult mice

In animal-based research, welfare assessments are essential for ethical and legal reasons. However, accurate assessment of suffering in laboratory animals is often complicated by the multidimensional character of distress and pain and the associated affective states. The present study aimed to design and validate multidimensional composite measure schemes comprising behavioral and biochemical parameters based on a bioinformatics approach. Published data sets from induced and genetic mouse models of neurological and psychiatric disorders were subjected to a bioinformatics workflow for cross-model analyses. ROC analyses pointed to a model-specific discriminatory power of selected behavioral parameters. Principal component analyses confirmed that the composite measure schemes developed for adult or young mice provided relevant information with the level of group separation reflecting the expected severity levels. Finally, the validity of the composite measure schemes developed for adult and young mice was further confirmed by k-means-based clustering as a basis for severity classification. The classification systems allowed the allocation of individual animals to different severity levels and a direct comparison of animal groups and other models. In conclusion, the bioinformatics approach confirmed the suitability of the composite measure schemes for evidence-based comparative severity assessment in adult and young mice. In particular, we demonstrated that the composite measure schemes provide a basis for an individualized severity classification in control and experimental groups allowing direct comparison of severity levels across different induced or genetic models. An online tool (R package) is provided, allowing the application of the bioinformatics approach to severity assessment data sets regardless of the parameters or models used. This tool can also be used to validate refinement measures.