Revolutionizing High Content Analysis: Advancing Drug Discovery by combining complex in vitro models with AI-Powered 3D/4D image analysis

The FDA's approval of organoids in preclinical research, under the FDA Modernization Act 2.0, has catalyzed a major shift toward complex in vitro models (CIVM) for drug testing and disease research. The implementation of more biologically relevant phenotypic model formats, however, requires an evolution of imaging and analysis techniques. Traditional 2D high-content analysis (HCA) approaches fall short when it comes to capturing 3D biological information in detail, and many 3D cell culturing methods suffer from high sample variability. Modern microscopy systems now generate detailed 3D/4D datasets of CIVM, offering unprecedented insights into cellular structures but HCA image analysis software has not progress on par with the acquisition systems. There is the need of 3D/ 4D high content image analysis solutions that can not only analyze a whole 3D structure without compression or sacrificing Z information but that the speed of the analysis can match the speed of the acquisition so that image analysis or extraction of information from such images do not constitute a bottle neck during the discovery phase.

Here we present the ZEISS arivis software platform that address these challenges and enables researchers to harness the full potential of CIVM for drug discovery and development. At the core of the platform is ZEISS arivis well stablished technology to handle big data files of any dimensionality. It includes cutting-edge tools that uses AI and deep learning segmentation for reliable and reproduceable analysis pipelines, AI models that fit the researcher needs can be trained in their own application using minimal amount of data.