Imaging in the context of 3R: chances and threats

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During the previous funding periods, we gained important insights into the effects of imaging techniques (Magnetic Resonance Imaging (MRI), Ultrasound (US), and Computed Tomography (CT)) on animal welfare and study outcomes in healthy and tumor-bearing mice. The tested imaging protocols were well tolerated according to our severity tests. However, CT and contrast-enhanced US induced changes in tumor physiology (esp. tumor perfusion and immune reactions), which were further investigated through a systematic review. Another question was whether state of the art severity tests are sensitive enough to detect even mild burdens and tissue reactions and whether this can be refined by imaging applications. Therefore, we implemented an imaging concept for the simultaneous detection of pain ([18F]Fluspidine-PET-CT) and tissue damage ([99mTc]-Duramycin-SPECT-CT). By using this new concept, we have already validated the suitability of a postoperative analgesia protocol in a rat model of partial liver resection. The results indicate that our approach can significantly enhance pain monitoring in future preclinical studies and be a reference for other severity assessment tools. Currently, we are investigating the suitability of these tracers in models of neuropathic pain. The 3rd funding period will focus on an in-depth analysis of previously collected behavioral and physiological welfare data. Thereby, the data will be re-analyzed to significantly improve the generalizability and robustness of the RElative Severity Assessment (RELSA) tool. Additionally, we plan to utilize the RELSA tool to design and publish appropriate score sheets for imaging protocols. By comparing these datasets with those from other working groups within our consortium and conducting further systematic reviews (e.g., CT effects on tumor physiology), we aim to develop comprehensive recommendations for “refining” imaging techniques (e.g., appropriate anesthesia protocols). During the previous funding periods, we have already published some of the imaging data as a CT reference database, making it freely accessible to the scientific community in accordance to FAIR criteria. In the 3rd funding period, we plan to create an expanded and comprehensive imaging database comprising data from various imaging modalities and disease models. This database will be easily accessible and enable data reuse, thereby promoting the long-term reduction of laboratory animals. Lastly, we aim to develop comprehensive recommendations for the efficient and reliable use of imaging for the refinement of experiments and the assessment of their severity. We will accomplish this by detailed comparison of our pre-existing data, focusing on the sensitivity of our imaging protocols in contrast to conventional methods.