An AI Foundation Toxicology Model and Framework to Support Waiving a Second Species in Drug Safety Studies

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Expected Impact: The action under this topic is expected to achieve the following impacts: Faster and more informed decision-making through the use of an AI-driven NAM (AI Foundation Toxicology Model) and increased efficiency through rapid processing of vast amounts of data [1]. Increased consistency and standardisation in a NAM-based approach, specifically an AI model, used by industry in the efficient development, testing and production of safe and effective innovative health technologies, improving industrial competitiveness. Regulatory adoption of a NAM-enabled second species waiver model (AI Foundation Toxicology Model) and weight-of-evidence framework, in line with recommendations and more consistent global decision-making on waiving second species testing. Reduction in animal use, accelerated timelines and lower costs, enhancing the competitiveness of the European health industry through economical and ethical benefits. Improved public health as patients will benefit from safe and effective medicines developed faster using validated NAMs. The action is expected to contribute to the EU Directive (2010/63/EU) [2] on the protection of animals used for scientific purposes and the implementation of the 3Rs principles to replace, reduce and refine the use of animals. The action is expected also to consider and contribute to EU programmes, initiatives and policies on New Approach Methodologies (NAMs) such as the future European Research Area (ERA) action on accelerating NAMs to advance biomedical research and testing of medicinal products and medical devices. [1] Transforming animal study toxicology reports into structured, harmonized data using large language models [2] European Directive on the protection of animals used for scientific purposes Expected Outcome: The action under this topic must contribute to all of the following outcomes: A validated Artificial Intelligence (AI) Foundation Toxicology Model* that provides transparent probabilistic predictions for industry and regulator stakeholders to determine when a second species in chronic (>90 days) and sub-chronic (90 days) small molecule medicine repeat-dose studies is unlikely to provide additional safety relevant information, including risks of missed toxicity, organ-specific findings, and divergence in No Observed Adverse Effect Level (NOAEL). The goal would be to enable waiving the need for two species chronic testing for small molecules and other modalities e.g. oligonucleotides. A standardised, transparent weight-of-evidence framework for industry, regulator and academic stakeholders that enables reproducible assessment of evidence quality, consistency, relevance, and uncertainty across regulatory submissions, supporting the wider adoption of the AI Foundation Toxicology Model and New Approach Methodology (NAM)-based toxicology strategies in general. Functional tools, templates, and training materials that support the real-world implementation, sustainability and evolution of the…