Innovative Health Initiative JU Call 13 · An AI Foundation Toxicology Model and Framework to Support Waiving a Second Species in Drug Safety Studies

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] (http://pubmed.ncbi.nlm.nih.gov/42133063/). • Increased consistency and standardisation in a NAM-based approach, specifically an AI model, used by industry in the efficient development, testing and production…

The action under this topic must contribute to all of the following outcomes: 1. 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,…

Regulatory guidelines e.g. ICH M3(R2) for nonclinical safety assessment of new small molecule medicines have historically required toxicity studies in two species, typically one rodent and one non-rodent, to maximise the likelihood of detecting adverse effects, improve the translatability to humans and guide patient dosing and monitoring. This two species approach is increasingly challenged by ethical considerations, long and costly study durations, limited reproducibility,…