Advancing Human-Centric Translational Research using Computational Approaches

Over 90% of drug candidates fail in clinical development — largely because traditional animal models fall short of predicting human outcomes. As new therapeutic modalities like antibodies, cell and gene therapies emerge, this translation gap is only widening.

“The time to integrate NAMs into drug development is now.”

Human-relevant NAMs — including advanced in vitro systems, in silico mechanistic models, and AI-driven analytics — offer a more predictive, ethical, and efficient path forward. They enable earlier, data-informed decisions that improve clinical success rates, and are backed by growing regulatory support.