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.
