3D-BrAIn at a Glance
3D-BrAIn is an EU-funded research project aimed at developing personalized precision medicine for central nervous system (CNS) disorders. The project involves building a bio-digital twin model of the human brain that is precise, personalized, and predictive.
The revolutionary 3D-BrAIn high-precision CNS platform will allow robust and accurate modelling of the CNS across a broad range of neuropsychiatric diseases, including epilepsy, autism, and schizophrenia, which are all diseases that are difficult to treat due to the complexity of the CNS and the variability between individuals. The 3D-BrAIn platform has the potential to improve our understanding of CNS disorders, facilitate the development of effective treatments, and enhance patient outcomes.
The 3D-BrAIn project uses breakthrough technology, including stem cell technologies, microelectrode array technology, and artificial intelligence, to create a comprehensive and physiologically representative platform for personalized medicine, drug screening, and neurotoxicity testing of the CNS. The project aims to have this technology ready to enter the drug development market by 2028.
3D-BrAIn will bring together 3 groundbreaking technologies to achieve its goal of revolutionizing the treatment of CNS disorders in a personalized, precise, and predictive fasion, including:
- A novel, highly reproducible human brain modelling technology that uses robust induced pluripotent stem cell (iPSC)-derived 3D adherent cortical organoid cultures;
- A unique, state-of-the-art 3D multielectrode array (MEA) technology for non-invasive high resolution electrophysiological recordings;
- A novel approach to analyse and interpret the large quantities of functional data using tailored automated machine learning (ML)-based algorithms.
In this project, a prototype of the 3D-BrAIn platform will be developed by growing functional 3D organoids that resemble the human cortex on 3D MEA micropillar electrodes (allowing continuous functional monitoring) and by developing ML-based algorithms that can process and interpret the large spatiotemporal data sets. Once all individual components are optimized and integrated, proof-of-concept will be obtained by validating the platform for two of the envisaged applications: CNS drug development and neurotoxicity screening.