Animal models have been the mainstay for testing hypotheses about human diseases and their treatment in academic research and drug development. However, these models raise ethical concerns and are costly and time-consuming. Most importantly, animal models often have low predictive power for the behavior of novel treatments in humans. Ethically, animal testing is arguably justifiable only if it can meaningfully predict outcomes in humans. Financially, committing millions of dollars and years of development to models with limited informative value is equally hard to defend. As governments and regulators, such as the US FDA, push to reduce animal testing, researchers from academia, pharma, and biotech are increasingly turning to new approach methodologies (NAMs) for preclinical and nonclinical testing. In this episode of the BioRevolution podcast, Louise von Stechow and Andreas Horchler discuss what is needed to shift from animal testing to NAM models which could be more meaningful for human biology. In particular, AI-based approaches offer a scalable alternative to animal testing that can make predictions based on a variety of data sources to provide a better understanding of human disease biology. Disclaimer: Louise von Stechow & Andreas Horchler and their guests express their personal opinions, which are founded on research on the respective topics, but do not claim to give medical, investment or even life advice in the podcast.

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