Descrizione del progetto
Una piattaforma di dati olistica per accelerare lo sviluppo di farmaci e sostanze chimiche migliori e più sicuri
La caratterizzazione della potenziale tossicità delle sostanze chimiche e dei prodotti farmaceutici è cruciale per la sicurezza umana e ambientale, nonché per la competitività industriale. Normative sempre più rigorose e le pressioni per ridurre la sperimentazione animale hanno condotto a enormi progressi sui metodi in vitro e in silico. Tra i molteplici nuovi strumenti a disposizione dei ricercatori figurano i modelli di meccanismi d’azione degli eventi avversi, i saggi molecolari e la tossicogenomica. L’integrazione di questi metodi attualmente eterogenei in un’unica piattaforma di dati è il fulcro del progetto ARCHIMEDES, finanziato dall’UE. Sfruttando l’IA e la scienza dei megadati, la piattaforma di grafico di conoscenza relativo alla tossicologia del progetto sosterrà lo sviluppo di farmaci e sostanze chimiche dalle prestazioni superiori, più sicuri e maggiormente sostenibili.
Obiettivo
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.
Campo scientifico
- 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
Programma(i)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Argomento(i)
Meccanismo di finanziamento
HORIZON-AG - HORIZON Action Grant Budget-BasedIstituzione ospitante
33100 Tampere
Finlandia