Selecting the Right Predictive & Reproducible Tumor Models to Better Dissect Mechanisms of Action, Facilitate Biomarker Discovery to Empower Patient Selection & Bridge the Preclinical-to-Clinical Disconnect
Following the first‑ever IND approval supported by organoid‑only efficacy data from SillaJen, alongside the FDA’s March 2026 draft guidance on New Approach Methodologies (NAMs) - providing much‑needed clarity on how to confidently integrate NAMs into regulatory submissions - and continued global regulatory momentum to reduce reliance on animal testing, the oncology field is entering a new era of human‑relevant drug development. Advances in tumor model engineering are further accelerating this shift, enabling more sophisticated interrogation of emerging modalities and targets.
Yet, despite this progress, the core challenge remains unchanged: ensuring preclinical models truly translate to the clinic. As more innovative therapies advance into first‑in‑human trials, too many still fail due to suboptimal model selection, driving escalating R&D costs and delayed timelines. In this rapidly shifting landscape, the need to de‑risk earlier and build confidence in translational data has never been greater.
Attending Companies Include