Descripción del proyecto
Una plataforma de datos holística para acelerar el desarrollo de fármacos y productos químicos mejores y más seguros
Caracterizar la posible toxicidad de fármacos y productos químicos es esencial para garantizar la seguridad humana y ambiental, así como para fomentar la competitividad de la industria. Las normativas cada vez más estrictas y el impulso para reducir los ensayos con animales han favorecido grandes avances en los métodos «in vitro» e «in silico». Los modelos de rutas de resultados adversos, los ensayos moleculares y la toxicogenómica son solo algunas de las nuevas herramientas a disposición de los investigadores. El objetivo del proyecto ARCHIMEDES, financiado con fondos europeos, es combinar estos métodos individuales y fragmentados en una sola plataforma de datos. La plataforma Toxicology Knowledge Graph del proyecto emplea la inteligencia artificial y la ciencia de los datos masivos para respaldar el desarrollo de fármacos y productos químicos más seguros y sostenibles con un mayor rendimiento.
Objetivo
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.
Ámbito científico
- 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
Programa(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régimen de financiación
HORIZON-AG - HORIZON Action Grant Budget-BasedInstitución de acogida
33100 Tampere
Finlandia