Improving Clinical Translation For ADCs And Immuno-Oncology With Human-Relevant Tumor Models

Compelling preclinical results that fail to translate clinically remain one of the most costly problems in oncology drug development, and limitations in conventional tumor models are a major contributing factor. Models that don't adequately reflect tumor heterogeneity, architecture, or the microenvironment can obscure how a candidate will actually perform.

This webinar examines patient-derived tumor organoids and advanced 3D models as more predictive tools for evaluating antibody-drug conjugates and immuno-oncology therapies. The session addresses specific mechanistic factors, including target expression, internalization, cellular uptake, tumor-cell killing, and immune-mediated response, in models designed to better reflect clinical conditions. Viewers will also gain perspective on where these approaches fit within earlier development stages to sharpen candidate selection and reduce uncertainty before committing to in vivo or clinical work. Explore the full session to see how these models are applied in practice.