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BEHANDLING-God og treffsikker diagnostikk, behandling og rehabilitering

Improved personalized medicine through machine learning in mental disorders (IMPLEMENT)

Tildelt: kr 2,9 mill.

Psykoselidelser er alvorlige psykiske sykdommer med tidlig debut og kan ha et kronisk, livslangt forløp. Disse lidelsene fører til et stort trykk på helsevesenet og koster samfunnet omtrent 100 milliarder årlig i Europa alene. Årsakene til psykiske lidelser er ikke fullt ut forstått, og det eksisterer foreløpig ingen objektive verktøy som kan hjelpe til med diagnostisering eller valg av behandlingsmetoder. Hos noen pasienter kan dette føre til perioder med mangelfull og ineffektiv behandling. For å adressere dette har IMPLEMENT (I) med suksess utviklet og anvendt nye maskinlæringsmetoder for identifisering av biologiske profiler assosiert med schizofreni som nå brukes for subgruppeidentifikasjon, (II) optimalisert en multimodal behandlingsrespons-prediksjonsmodell, som oppnår en nøyaktighet på 94%, (III) utført omfattende arbeid på den prekliniske, mekanistiske karakteriseringen av kognitive prosesser relevante for schizofreni, samt tilhørende metodeutvikling, og (IV) utviklet en IKT-løsning for den integrerte bearbeidingen og styringen av gendata. Med dette ønsker IMPLEMENT å danne grunnlaget for Samlet sett vil IMPLEMENT gi grunnlag for biologisk tilpasset behandling for schizofreni, og imøtekomme et stort og umøtt behov i et område der det foreløpig ikke finnes noen robuste kliniske stratifiseringsverktøy på individnivå.

Work performed as part of the IMPLEMENT project had a strong impact on several positive developments. First, it contributed to mechanisms informed artificial intelligence (AI) to become an important scientific focus in the upcoming, structurally-funded German Center for Mental Health (GCMH), in which IMPLEMENT partner sites CIMH and LMU are coordinating partners. Furthermore, it contributed to the implementation of a national infrastructure with focus on biobanking, omics, and bioinformatics within the GCMH, which is coordinated by E.Schwarz (CIMH). This infrastructure will serve as a knowledge hub to disseminate AI approaches, such as those developed in IMPLEMENT, across the GCMH and beyond. Similarly, machine learning approaches, such as the multimodal stacked generalization and sequential prognostic approach developed in IMPLEMENT (LMU) will likely form a core part of the personalized medicine initiatives pursued in the network. Furthermore, experience in collaborative research and data management made within IMPLEMENT, led to the development of a novel ICT solution by E.Schwarz (CIMH), the foldercase research management platform (www.foldercase.com). It is a freely available scientific project, collaboration and communication management tool, which is already used by approximately 860 scientists, as well as national and international research networks. We expect the platform to form a major technological pillar in the new German Center for Mental Health, fostering collaborative research, data FAIRization, and the development of data science solution. The platform has already been central for the ongoing applications of several large-scale research networks (BMBF-funded SFB, “Sonderforschungsbereich”). IMPLEMENT further played an important role for the award of the annual Chica and Heinz Schaller Research Award (100,000€), and the call to a full professorial position (Hector Institute for Artificial Intelligence in Psychiatry), both to E.Schwarz. IMPLEMENT further led to the initiation of several new research collaborations, including with the Douglas Mental Health University Institute, Dr. Herrmann, University of Heidelberg; Prof. Cecil, Erasmus MC, Prof. Esther Walton, University of Bath, and The H2020 project Early Cause, as well as with members of the FP7 project HELIX. IMPLEMENT results further contributed to a number of successful grant applications focused on the development and application of machine learning technology in psychiatry.

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.

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BEHANDLING-God og treffsikker diagnostikk, behandling og rehabilitering