Projektbeschreibung
Ganzheitliche Datenplattform zur beschleunigten Entwicklung besserer und sicherer Arzneimittel und Chemikalien
Die potenzielle Toxizität von Chemikalien und Arzneimitteln einschätzen zu können, ist sowohl für die Sicherheit von Mensch und Umwelt als auch für die Wettbewerbsfähigkeit der Industrie von grundlegender Bedeutung. Immer strengere Vorschriften und das Bestreben um die Reduzierung von Tierversuchen haben enorme Fortschritte bei den In-vitro- und In-silico-Verfahren nach sich gezogen. Modelle für unerwünschte Nebenwirkungen, molekulare Assays und Toxikogenomik sind nur einige der vielen, den Forschenden zur Verfügung stehenden neuen Instrumente. Die Zusammenfassung dieser gegenwärtig vereinzelten Methoden auf einer einzigen Datenplattform bildet den Mittelpunkt des EU-finanzierten Projekts ARCHIMEDES. Mithilfe von künstlicher Intelligenz und Big-Data-Wissenschaft wird die Toxikologie-Wissensgraphen-Plattform des Projekts die Entwicklung leistungsfähigerer, sichererer und nachhaltigerer Medikamente und Chemikalien unterstützen.
Ziel
Traditional in vivo tesTraditional in vivo tests are hampering the development of new, safe and effective chemicals and drugs. If on one hand we need to ensure that dangerous chemicals do not emerge, on the other, we also need to promote rapid and sustainable innovation to successfully overcome the modern challenges of humankind. Toxicogenomics aims at clarifying the mechanism of action (MOA) of chemicals by using omics assays. The Adverse Outcome Pathways (AOP) concept is also emerging to contextualise toxicogenomics-derived MOA. Efforts are ongoing to anchor AOPs to molecular assays, but systematic embedding of AOP-derived in vitro tests and Integrated Approaches to Testing and Assessment (IATA) are still unestablished. At the same time, toxicogenomics-based evidence still struggles to gain regulatory acceptance. I aim to implement an integrated strategy based on state-of-the-art big data science, artificial intelligence (AI), toxicogenomics, molecular assays and cell technology via a novel Knowledge Graph approach. I will do so by developing the Toxicology Knowledge Graph (TKG), an innovative data platform where the currently fragmented knowledge in the field is going to be curated and integrated. The TKG will serve as a learning platform for artificial intelligence (AI) algorithms, which will be used to: 1) find new characteristics of chemicals/drugs; 2) infer associations between exposures and diseases; 3) select the most relevant cell lines to study specific phenotypes/chemical classes; 4) find the best genes to be used as reporters for specific AOPs; 5) define the applicability domain of computational, experimental and IATA models. I will also establish and validate regulatory-relevant high-throughput molecular assays to investigate the point of departure (PoD) of exposures. The ARCHIMEDES project will shift the paradigm of chemical and drug development, facilitating the emergence of new, smarter, greener, and more sustainable chemicals, drugs and materials.
Wissenschaftliches Gebiet
- medical and health sciencesbasic medicinepharmacology and pharmacydrug discovery
- natural sciencescomputer and information sciencesartificial intelligence
- natural sciencescomputer and information sciencesknowledge engineering
- medical and health sciencesbasic medicinetoxicology
- medical and health sciencesmedical biotechnologycells technologies
Programm/Programme
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Thema/Themen
Finanzierungsplan
HORIZON-AG - HORIZON Action Grant Budget-BasedGastgebende Einrichtung
33100 Tampere
Finnland