Periodic Reporting for period 1 - ARCHIMEDES (dAta-dRiven integrated approaches to CHemIcal safety assessMEnt and Drug dEvelopment)
Período documentado: 2022-09-01 hasta 2025-02-28
Traditional toxicology has long relied on in vivo testing to assess the safety of chemicals and drugs, focusing primarily on observable phenotypic effects without uncovering the underlying molecular mechanisms. While this approach has been useful for hazard identification, it is often time-consuming, ethically problematic, and poorly suited for predicting the effects of novel, untested compounds. In contrast, mechanistic toxicology aims to elucidate the molecular mechanisms of toxicity by integrating high-throughput omics technologies, such as genomics, transcriptomics, and proteomics, to identify key biological pathways affected by chemical exposure. Building on this, systems toxicology takes a holistic approach, combining computational modeling, big data analytics, and molecular biology to predict toxicity outcomes based on a chemical’s intrinsic properties and biological interactions. This shift is critical as it enables more accurate, faster, and mechanistically informed risk assessments, reducing dependence on animal testing while improving regulatory decision-making. By leveraging integrated, data-driven approaches, mechanistic and systems toxicology provide a powerful alternative to traditional methods, offering predictive, reproducible, and human-relevant insights into the safety and efficacy of chemicals and drugs.
Despite efforts to modernize toxicology, the field remains fragmented, with limited interdisciplinary collaboration. ARCHIMEDES proposes a holistic, AI-driven framework to unify toxicological research, akin to the Google Knowledge Graph, by mapping unrelated knowledge domains and systematically validating inferred associations. Ultimately, this initiative position toxicology at the forefront of innovation, enabling safer, smarter, and more sustainable chemicals and pharmaceuticals while aligning with global efforts to reduce reliance on animal testing.
Despite efforts to modernize toxicology, the field remains fragmented, with limited interdisciplinary collaboration. ARCHIMEDES proposes a holistic, AI-driven framework to unify toxicological research, akin to the Google Knowledge Graph, by mapping unrelated knowledge domains and systematically validating inferred associations. Ultimately, this initiative position toxicology at the forefront of innovation, enabling safer, smarter, and more sustainable chemicals and pharmaceuticals while aligning with global efforts to reduce reliance on animal testing.
Traditional toxicology has long relied on in vivo testing to assess the safety of chemicals and drugs, focusing primarily on observable phenotypic effects without uncovering the underlying molecular mechanisms. While this approach has been useful for hazard identification, it is often time-consuming, ethically problematic, and poorly suited for predicting the effects of novel, untested compounds. In contrast, mechanistic toxicology aims to elucidate the molecular mechanisms of toxicity by integrating high-throughput omics technologies, such as genomics, transcriptomics, and proteomics, to identify key biological pathways affected by chemical exposure. Building on this, systems toxicology takes a holistic approach, combining computational modeling, big data analytics, and molecular biology to predict toxicity outcomes based on a chemical’s intrinsic properties and biological interactions. This shift is critical as it enables more accurate, faster, and mechanistically informed risk assessments, reducing dependence on animal testing while improving regulatory decision-making. By leveraging integrated, data-driven approaches, mechanistic and systems toxicology provide a powerful alternative to traditional methods, offering predictive, reproducible, and human-relevant insights into the safety and efficacy of chemicals and drugs.
Despite efforts to modernize toxicology, the field remains fragmented, with limited interdisciplinary collaboration. ARCHIMEDES proposes a holistic, AI-driven framework to unify toxicological research, akin to the Google Knowledge Graph, by mapping unrelated knowledge domains and systematically validating inferred associations. Ultimately, this initiative position toxicology at the forefront of innovation, enabling safer, smarter, and more sustainable chemicals and pharmaceuticals while aligning with global efforts to reduce reliance on animal testing.
Despite efforts to modernize toxicology, the field remains fragmented, with limited interdisciplinary collaboration. ARCHIMEDES proposes a holistic, AI-driven framework to unify toxicological research, akin to the Google Knowledge Graph, by mapping unrelated knowledge domains and systematically validating inferred associations. Ultimately, this initiative position toxicology at the forefront of innovation, enabling safer, smarter, and more sustainable chemicals and pharmaceuticals while aligning with global efforts to reduce reliance on animal testing.
Thus far, the ARCHIMEDES project has made significant progress in developing data-driven approaches for chemical safety assessment and drug development. A major achievement is the construction of a knowledge graph with over 60 million nodes and 2 billion edges, which has facilitated disease mapping and chemical exposure analysis. The project successfully linked human phenotypes, toxicology, and cheminformatics, leading to the identification of disease-drug and disease-chemical relationships. Additionally, One Health models were developed to analyze the molecular response to nanoparticulate exposure across species, advancing environmental health monitoring.
In computational and experimental toxicology, the project leveraged in vitro models to study pulmonary tissue responses, identifying biomarkers and candidate drugs for pulmonary fibrosis treatment. The research has also enhanced the Adverse Outcome Pathways (AOP) framework, integrating toxicogenomics data for better in vitro-in vivo extrapolation. A key innovation is the design of high-throughput PCR assays, which enable large-scale screening of chemicals with minimal reliance on animal testing.
Four specific major achievements have characterised the first two years of the project:
1. The development of the first One Health model of molecular response to nanoparticulate exposure (del Giudice et al. Nature Nanotechnology 2023).
2. The development of a new multi-layer classification of human diseases based on information collected in our knowledge graph (Möbus et al. Advanced Science 2024).
3. The development of a new analytical framework to anchor molecular observations to the AOP framework (Saarimäki et al. Advanced Science 2023).
4. The expansion of the above-mentioned methods by allowing the mapping of network representations of toxicogenomics data onto the AOP network (del Giudice et al. Advanced Science).
In computational and experimental toxicology, the project leveraged in vitro models to study pulmonary tissue responses, identifying biomarkers and candidate drugs for pulmonary fibrosis treatment. The research has also enhanced the Adverse Outcome Pathways (AOP) framework, integrating toxicogenomics data for better in vitro-in vivo extrapolation. A key innovation is the design of high-throughput PCR assays, which enable large-scale screening of chemicals with minimal reliance on animal testing.
Four specific major achievements have characterised the first two years of the project:
1. The development of the first One Health model of molecular response to nanoparticulate exposure (del Giudice et al. Nature Nanotechnology 2023).
2. The development of a new multi-layer classification of human diseases based on information collected in our knowledge graph (Möbus et al. Advanced Science 2024).
3. The development of a new analytical framework to anchor molecular observations to the AOP framework (Saarimäki et al. Advanced Science 2023).
4. The expansion of the above-mentioned methods by allowing the mapping of network representations of toxicogenomics data onto the AOP network (del Giudice et al. Advanced Science).