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.

Urinary neurotransmitter analysis and canine behavior assessment.

Behavioral problems are highly prevalent in domestic dogs, negatively affecting the quality of life of dogs and their owners. In humans and dogs, neuropsychological or neurobehavioral disorders can be associated with deviations in various neurotransmitter systems. Previous evidence has revealed correlations between urinary neurotransmitters and various behavioral disorders; however, a causal relationship has not yet been conclusively demonstrated. Non-invasive urinary neurotransmitter analysis may identify specific biomarkers, which enable a more differentiated assessment of canine behavioral disorders in the future and contribute to more effective neuromodulatory treatment decisions and monitoring. This approach could offer new insights into underlying pathomechanisms of canine neurobehavioral disorders. This study assessed urinary neurotransmitter levels and the descriptive behavior profile of 100 dogs using established rating scales (Canine Behavioral Assessment and Research Questionnaire, Attention Deficit Hyperactivity Disorder Rating Scale, Dog Personality Questionnaire, Canine Cognitive Dysfunction Rating Scale), and explored relationships between these variables. No correlation was found between urinary neurotransmitters and the assessed behavior profiles; however, age-, sex- and neuter-related influences were identified. The lack of correlation could be explained by the many confounding factors influencing both behavior and urinary neurotransmitter excretion, including age, sex and neuter status effects, and methodological issues e.g., low discriminatory power between anxiety and aggression in the descriptive behavior evaluation. Urinary neurotransmitter testing could not be validated as a tool for canine behavior evaluation in this study. However, reliable assessment methods with low susceptibility to human biases could be valuable in the future to support behavioral-phenotype diagnoses.

Voluntary wheel running behaviour as a tool to assess the severity in a mouse pancreatic cancer model.

Laboratory animals frequently undergo routine experimental procedures such as handling, restraining and injections. However, as a known source of stress, these procedures potentially impact study outcome and data quality. In the present study, we, therefore, performed an evidence-based severity assessment of experimental procedures used in a pancreatic cancer model including surgical tumour induction and subsequent chemotherapeutic treatment via repeated intraperitoneal injections. Cancer cell injection into the pancreas was performed during a laparotomy under general anaesthesia. After a four-day recovery phase, mice received either drug treatment (galloflavin and metformin) or the respective vehicle substances via daily intraperitoneal injections. In addition to clinical scoring, an automated home-cage monitoring system was used to assess voluntary wheel running (VWR) behaviour as an indicator of impaired well-being. After surgery, slightly elevated clinical scores and minimal body weight reductions, but significantly decreased VWR behaviour were observed. During therapy, body weight declined in response to chemotherapy, but not after vehicle substance injection, while VWR activity was decreased in both cases. VWR behaviour differed between treatment groups and revealed altered nightly activity patterns. In summary, by monitoring VWR a high impact of repeated injections on the well-being of mice was revealed and substance effects on well-being were distinguishable. However, no differences in tumour growth between treatment groups were observed. This might be due to the severity of the procedures uncovered in this study, as exaggerated stress responses are potentially confounding factors in preclinical studies. Finally, VWR was a more sensitive indicator of impairment than clinical scoring in this model.

Design of composite measure schemes for comparative severity assessment in animal-based neuroscience research: A case study focussed on rat epilepsy models.

Comparative severity assessment of animal models and experimental interventions is of utmost relevance for harm-benefit analysis during ethical evaluation, an animal welfare-based model prioritization as well as the validation of refinement measures. Unfortunately, there is a lack of evidence-based approaches to grade an animal's burden in a sensitive, robust, precise, and objective manner. Particular challenges need to be considered in the context of animal-based neuroscientific research because models of neurological disorders can be characterized by relevant changes in the affective state of an animal. Here, we report about an approach for parameter selection and development of a composite measure scheme designed for precise analysis of the distress of animals in a specific model category. Data sets from the analysis of several behavioral and biochemical parameters in three different epilepsy models were subjected to a principal component analysis to select the most informative parameters. The top-ranking parameters included burrowing, open field locomotion, social interaction, and saccharin preference. These were combined to create a composite measure scheme (CMS). CMS data were subjected to cluster analysis enabling the allocation of severity levels to individual animals. The results provided information for a direct comparison between models indicating a comparable severity of the electrical and chemical post-status epilepticus models, and a lower severity of the kindling model. The new CMS can be directly applied for comparison of other rat models with seizure activity or for assessment of novel refinement approaches in the respective research field. The respective online tool for direct application of the CMS or for creating a new CMS based on other parameters from different models is available at https://github.com/mytalbot/cms. However, the robustness and generalizability needs to be further assessed in future studies. More importantly, our concept of parameter selection can serve as a practice example providing the basis for comparable approaches applicable to the development and validation of CMS for all kinds of disease models or interventions.