APPW2025

Oranizers :Takahisa Murata (Veterinary Pharmacology, The University of Tokyo) and Masayuki Goto (Axcelead Drug Discovery Partners, Inc.)

Research in basic biological sciences such as physiology, anatomy, and pharmacology begin with understanding animals. However, many experiments involving animals are conducted by humans following established methods and producing results through visual inspection. The data obtained is often limited and faces numerous issues with objectivity, reproducibility, and extrapolation to humans. Achieving stable data can also require significant time. Animal experimentation, which can be considered a bottleneck in basic biological research, is seeing advancements through digital transformation (DX) initiatives. The U.S. NIH has allocated substantial funding to these efforts. We have also been developing technologies using imaging and artificial intelligence to non-invasively and unrestrainedly capture the physiological and psychological states of experimental animals in unbiased, multivariate numerical data. We are creating machine learning systems capable of identifying behaviors, facial expressions, and individuals among mice, rats, and monkeys, and applying these systems to experiments. This approach is resolving many issues associated with animal experiments, allowing for multimodal analysis with a minimal number of animals. In this symposium, researchers involved in the digital transformation of animal experiments in academia and industry will present case studies and discuss future challenges and prospects.