Expanding Data Foundations to Unlock Predictivity & Regulatory Acceptance of NAMs
- Understand how model performance is limited by the training dataset and why incomplete or narrow data subsets lead to reduced predictivity and continued reliance on in vivo studies
- Learn practical approaches from pharma using integrated in vitro and in silico data for screening while ensuring IND-enabling decisions remain grounded in translatable evidence
- Explore collaboration strategies between toxicologists and AI scientists to generate scalable datasets and build models that support future first-in-human decision making