About 3D-BrAIn
3D-BrAIn at a Glance
3D-BrAIn is an EU-funded research project focused on advancing personalised precision medicine for central nervous system (CNS) disorders. The project centres on creating a bio-digital twin model of the human brain that is precise, personalised, and predictive.
The groundbreaking 3D-BrAIn high-precision CNS platform will enable robust and accurate modelling of the CNS across a wide range of neuropsychiatric diseases, such as epilepsy, autism, and schizophrenia. These conditions are notoriously difficult to treat due to the complexity of the CNS and individual variability. The 3D-BrAIn platform aims to deepen our understanding of CNS disorders, support the development of effective treatments, and improve patient outcomes.
The 3D-BrAIn project leverages cutting-edge technologies, including stem cell science, microelectrode array technology, and artificial intelligence, to develop a comprehensive and physiologically representative platform for personalised medicine, drug screening, and neurotoxicity testing of the CNS. The goal is for this technology to be ready to enter the drug development market by 2028.
The Technology
3D-BrAIn will integrate three groundbreaking technologies to revolutionise the treatment of CNS disorders in a personalised, precise, and predictive manner:
- Human brain modelling technology: A novel, highly reproducible method using robust induced pluripotent stem cell (iPSC)-derived 3D adherent cortical organoid cultures.
- 3D multielectrode array (MEA) technology: A unique, state-of-the-art approach enabling non-invasive, high-resolution electrophysiological recordings.
- Advanced machine learning (ML) algorithms: Tailored automated ML-based methods for analysing and interpreting large quantities of functional data.
As part of the project, a prototype of the 3D-BrAIn platform will be developed. This involves growing functional 3D organoids resembling the human cortex on 3D MEA micropillar electrodes for continuous functional monitoring and creating ML-based algorithms capable of processing and interpreting large spatiotemporal datasets.
Once the individual components are optimised and fully integrated, proof-of-concept will be achieved by validating the platform for two key applications: CNS drug development and neurotoxicity screening.
