An AI Foundation Toxicology Model and Framework to Support Waiving a Second Species in Drug Safety Studies
Funds development of an AI toxicology model that lets drug developers waive costly second-species animal safety testing.
This IHI call funds an AI model meant to predict when testing a drug candidate in a second animal species won't add safety-relevant information, so drug developers can skip that step for small-molecule medicines without compromising patient safety. The proposal must build on named prior IMI/IHI projects -- BigPicture, eTRANSAFE, eTOX, imSAVAR and VICT3R -- plus initiatives like ASPIS and NC3Rs' Vi…
This topic funds a consortium building a validated AI Foundation Toxicology Model that predicts when a second-species chronic or sub-chronic toxicology study is unlikely to add safety-relevant information for small-molecule medicines. It also wants a standardised weight-of-evidence framework, plus tools and training material that help regulators, industry and academics adopt the model and cut reliance on animal testing.
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