Psychotic disorders are severe mental illnesses with early onset, frequently chronic course and often lifelong impairment. As a consequence, they cause an enormous healthcare burden, costing close to €100 billion annually in Europe alone. The biology of these illnesses is insufficiently understood and no objective tools exist to aid in diagnosis or treatment selection. This leads to long periods of inadequate and ineffective treatment, significantly limiting the opportunity for achieving more optimal clinical outcomes. To address this, IMPLEMENT will develop a translational research framework that identifies biomarkers for treatment-relevant stratification of the most severe psychotic disorder, schizophrenia. Building on known candidates, IMPLEMENT will use advanced machine learning on high-dimensional multi-OMICS and brain scans to identify illness-associated profiles indexing patient subgroups. Using big data approaches (n > 60,000), IMPLEMENT will explore the impact of genetic risk and neurodevelopmental processes on the formation of biological subgroups and use clinical studies of conventional antipsychotic treatment and innovative treatment approaches to tune subgroup profiles towards clinical utility. The IMPLEMENT framework will incorporate preclinical validation to leverage neurobiological understanding and optimize biological subgroup profiles. The clinical utility of these profiles will be validated in independent clinical samples and prospectively recruited subjects. IMPLEMENT will integrate these efforts with ICT development, to optimize the use of high-dimensional datasets across diverse repositories, to optimally harmonize data for personalized medicine investigations and safeguard patient privacy. Overall, IMPLEMENT will provide the basis for biologically-informed personalized medicine approaches in schizophrenia, addressing an enormous unmet medical need in an area of medicine in which currently no robust clinical stratification tools exist.
Project leader: Lars Tjelta Westlye
Institution: Psykologisk institutt