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the first‑ever IND approval supported by organoid‑only efficacy data from SillaJen, alongside the FDA’s March 2026 draft guidance on New Approach Methodologies (NAMs) - providing much‑needed clarity on how to confidently integrate NAMs into regulatory submissions - and continued global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.
Driven by Rapid Industry Momentum:
- AbbVie’s recent ADC approval underscores continued progress in targeted therapies
- Johnson & Johnson’s $1bn acquisition signals rising investment in next-gen modalities targeting difficult biology like KRAS
- Novartis’ multi-billion-dollar breast cancer deal highlights the race for more selective, better-tolerated therapies
- AstraZeneca’s evolving HER2 strategy continues to reshape standards of care
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