dAta-dRiven integrated approaches to CHemIcal safety assessMEnt and Drug dEvelopment
The ARCHIMEDES project aims to revolutionize chemical and drug development by integrating toxicogenomics, AI, and a Knowledge Graph to enhance safety and innovation in a regulatory-compliant manner.
Projectdetails
Introduction
Traditional 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 and AOP
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 contextualize toxicogenomics-derived MOA.
Current Challenges
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.
Proposed Strategy
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.
Development of the Toxicology Knowledge Graph (TKG)
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.
AI Learning Platform
The TKG will serve as a learning platform for artificial intelligence (AI) algorithms, which will be used to:
- Find new characteristics of chemicals/drugs;
- Infer associations between exposures and diseases;
- Select the most relevant cell lines to study specific phenotypes/chemical classes;
- Find the best genes to be used as reporters for specific AOPs;
- Define the applicability domain of computational, experimental, and IATA models.
High-Throughput Molecular Assays
I will also establish and validate regulatory-relevant high-throughput molecular assays to investigate the point of departure (PoD) of exposures.
Project Impact
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.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 2.000.000 |
Totale projectbegroting | € 2.000.000 |
Tijdlijn
Startdatum | 1-9-2022 |
Einddatum | 31-8-2027 |
Subsidiejaar | 2022 |
Partners & Locaties
Projectpartners
- TAMPEREEN KORKEAKOULUSAATIO SRpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
Project | Regeling | Bedrag | Jaar | Actie |
---|---|---|---|---|
Benchmarking AI to predict mutagenicity by combing chemical structure and genome-wide mutation dataMUTAPREDICT is an AI-driven platform that predicts the mutagenic potential of new drugs using whole genome sequencing, enhancing safety assessments while reducing reliance on animal testing. | ERC Proof of... | € 150.000 | 2024 | Details |
Understanding the impact of environmental POLlution on the adaptive Immune SystemThis project aims to utilize advanced omics technologies to investigate the low-concentration bioactivity of PFAS chemicals and their potential role in non-communicable diseases. | ERC Starting... | € 1.499.749 | 2025 | Details |
New methodologies for automated modeling of the dynamic behavior of large biological networksAUTOMATHIC aims to develop an automated framework for ODE modeling of cell transport and signaling to enhance drug safety and optimize therapies for chronic kidney disease patients. | ERC Starting... | € 1.500.000 | 2024 | Details |
Single-Cell Metabolomics for Drug Discovery and DevelopmentThe project aims to commercialize single-cell metabolomics technology to enhance drug safety by revealing off-target effects and metabolic responses in drug candidates. | ERC Proof of... | € 150.000 | 2022 | Details |
Developing a human-based stem cell model for reproductive toxicityDeveloping a high-throughput human-based assay for male reproductive toxicity to enhance drug safety testing and reduce reliance on animal models. | ERC Proof of... | € 150.000 | 2024 | Details |
Benchmarking AI to predict mutagenicity by combing chemical structure and genome-wide mutation data
MUTAPREDICT is an AI-driven platform that predicts the mutagenic potential of new drugs using whole genome sequencing, enhancing safety assessments while reducing reliance on animal testing.
Understanding the impact of environmental POLlution on the adaptive Immune System
This project aims to utilize advanced omics technologies to investigate the low-concentration bioactivity of PFAS chemicals and their potential role in non-communicable diseases.
New methodologies for automated modeling of the dynamic behavior of large biological networks
AUTOMATHIC aims to develop an automated framework for ODE modeling of cell transport and signaling to enhance drug safety and optimize therapies for chronic kidney disease patients.
Single-Cell Metabolomics for Drug Discovery and Development
The project aims to commercialize single-cell metabolomics technology to enhance drug safety by revealing off-target effects and metabolic responses in drug candidates.
Developing a human-based stem cell model for reproductive toxicity
Developing a high-throughput human-based assay for male reproductive toxicity to enhance drug safety testing and reduce reliance on animal models.
Vergelijkbare projecten uit andere regelingen
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QUANTUM-TOX - Revolutionizing Computational Toxicology with Electronic Structure Descriptors and Artificial IntelligenceThis project aims to revolutionize computational toxicology by developing interpretable quantum mechanics-based descriptors (ESigns) for accurate toxicity predictions across the entire chemical space. | EIC Pathfinder | € 1.994.770 | 2024 | Details |
Simultaneous Multiparametric MEA based platform for in-vitro chronic cardiotoxicity assessment with live-cell fluorescence imaging and electrophysiology.SiMulTox develops a novel platform for simultaneous long-term assessment of functional and structural cardiotoxicity, aiming to enhance drug safety evaluation and reshape the in-vitro testing market. | EIC Transition | € 786.875 | 2022 | Details |
IDEFIX Multiorgan toxicity and efficacy test platformCherry Biotech's IDEFIX project aims to revolutionize preclinical drug testing by developing a customizable organ-on-chip platform that mimics human multiorgan physiology, enhancing efficacy and toxicity predictions. | EIC Transition | € 2.496.073 | 2022 | Details |
A Multi-Omics Approach for Novel Drug Targets, Biomarkers and Risk Algorithms for Myocardial InfarctionTargetMI aims to rapidly discover novel drug targets and biomarkers for myocardial infarction using a high-throughput multi-omic approach on 1000 samples, enhancing clinical risk prediction and translation. | EIC Pathfinder | € 3.999.840 | 2023 | Details |
QUANTUM-TOX - Revolutionizing Computational Toxicology with Electronic Structure Descriptors and Artificial Intelligence
This project aims to revolutionize computational toxicology by developing interpretable quantum mechanics-based descriptors (ESigns) for accurate toxicity predictions across the entire chemical space.
Simultaneous Multiparametric MEA based platform for in-vitro chronic cardiotoxicity assessment with live-cell fluorescence imaging and electrophysiology.
SiMulTox develops a novel platform for simultaneous long-term assessment of functional and structural cardiotoxicity, aiming to enhance drug safety evaluation and reshape the in-vitro testing market.
IDEFIX Multiorgan toxicity and efficacy test platform
Cherry Biotech's IDEFIX project aims to revolutionize preclinical drug testing by developing a customizable organ-on-chip platform that mimics human multiorgan physiology, enhancing efficacy and toxicity predictions.
A Multi-Omics Approach for Novel Drug Targets, Biomarkers and Risk Algorithms for Myocardial Infarction
TargetMI aims to rapidly discover novel drug targets and biomarkers for myocardial infarction using a high-throughput multi-omic approach on 1000 samples, enhancing clinical risk prediction and translation.