Project description
A holistic data platform to accelerate the development of better and safer drugs and chemicals
Characterising the potential toxicity of chemicals and pharmaceuticals is critical to human and environmental safety and to industry competitiveness. Increasingly stringent regulations and a push to reduce animal testing have led to tremendous progress regarding in vitro and in silico methods. Adverse outcome pathways models, molecular assays and toxicogenomics are among the many new tools available to researchers. Integrating these currently disparate methods into a single data platform is the focus of the EU-funded ARCHIMEDES project. Harnessing AI and big data science, the project’s Toxicology Knowledge Graph platform will support the development of higher performing, safer and more sustainable drugs and chemicals.
Objective
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.
Fields of science
- 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
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-AG - HORIZON Action Grant Budget-BasedHost institution
33100 Tampere
Finland