iMOUSE - Reforming the Strategy of Refinement and Reduction for indispensable laboratory animal-based studies in translational research

Abstract

Considering the intricate nature of biological processes within organisms, it is undeniable that relying solely on in vitro-generated primary-cell-like cultures or organ-like products in preclinical and basic research is insufficient to replace animal-based studies fully. This limitation is particularly significant when considering the regulations enforced by legislative assemblies worldwide. The necessity of animal-based studies to approve chemicals and medications. In contradiction, European countries aim to banish animal-based studies. Therefore, we must understand the impact of the data refinement and experiment replacement strategy we will introduce here. This project aimed to revolutionize data acquisition in animal-based studies by transforming manual observation into a reliable digital process. Reliable digital data will be generated by having the potential to reduce human bias by simply reducing human interaction. Additionally, reducing human interaction will reduce the severity levels due to stress reduction, fulfilling the 3R principles. Therefore, the first goal was to develop and implement a scalable, stable, running, and remotely accessible camera-based monitor system (the iMouse solution). At the same time, the target was to develop a retrofit solution (DigiFrame) for existing home-cage systems, not interfering with the regular workflow in animal facilities. As a result, we developed a digital monitoring system, named iMouseTV platform based on existing open-source software, allowing users to observe, record, share, and review animal-based studies within the home cage anytime from anywhere, reducing the stress level for the animals. Our systems first Proof of concept ran for over two years at the LIV in Hamburg. We also investigated an effective way to reduce data generation by setting up specific zones for detecting the motion of choice (e.g., drinking, food intake). The data sets can be stored, shared, and reviewed by users and refined by algorithms aiming to recognize the dedicated motions of the animals automatically. The implementation of the ML algorithms allows the iMouse solution to recognize whether an individual mouse was drinking and for how long and store results in the annotated video file and graph format. However, the identification and continuous tracking of the species is still in progress. In conclusion, we established a scalable human-independent monitoring and recording system, which can be implemented into the existing structures of institutions and companies without changing handling processes, to monitor animals and observe them by getting reliable digital data. Moreover, it is fundamental for automatic recognition within animal-based studies based on Artificial Intelligence.