Drug-induced liver injury (DILI) remains a leading cause of drug attrition during clinical development and post-marketing withdrawal, with nearly half of clinically significant DILI events escaping detection in traditional preclinical animal studies. This translational gap has led to the development of AI-enabled new approach methodologies that improve DILI prediction while advancing the 3Rs (replacement, reduction, and refinement) principles. This public webinar will explore how AI can support more accurate, efficient, and human-relevant approaches to assessing DILI.
During this webinar, our speaker will:
- Introduce AI-driven approaches for predicting DILI risk and assessing drug-induced toxicities
- Share examples from the FDA’s AI4Tox research program, including predictive and generative AI models and AI agents
- Explore how these approaches can complement, reduce, or replace selected animal studies
- Discuss the FDA’s ISTAND pilot program and its role in advancing novel AI-based drug development tools
Collectively, these advances point toward a future in which AI augments regulatory science by improving translational predictivity, accelerating drug development, reducing reliance on animal testing, and enabling safer medicines.
Speaker:
- Weida Tong, Ph.D., Director of Bioinformatics and Biostatistics, FDA/NCTR
This event does not constitute nor should be considered as any type of endorsement by TransCelerate or its members of any vendor, product, or technology. TransCelerate webinars may be recorded in whole or in part.