Advancing ADC Development: Leveraging AI and Translational Preclinical Models to Improve Clinical Predictability

Join our webinar to discover how to de-risk ADC development through the integration of AI-driven analytics, mouse clinical trials and specialized translational models.

Antibody–Drug Conjugates represent one of the fastest-growing therapeutic modalities in oncology, combining the specificity of monoclonal antibodies with the potency of cytotoxic payloads. Despite significant commercial and clinical success, ADC development remains highly complex. Challenges include target selection, linker stability, payload toxicity, resistance mechanisms, biomarker identification, and limited predictability of preclinical efficacy models. Many ADC programs demonstrate promising preclinical data yet encounter unexpected toxicity, insufficient efficacy, or resistance in clinical trials.

Bridging the gap between preclinical modeling and clinical performance requires more sophisticated and translationally relevant strategies. In this webinar, we will discuss how AI-enabled data mining and predictive modeling can support rational ADC design and biomarker discovery. We will also explore the value of mouse clinical trial platforms and ADC-resistant tumor models in understanding treatment response, resistance mechanisms, and combination strategies. By integrating advanced computational approaches with clinically relevant in vivo systems, we aim to improve translational confidence and accelerate decision-making in ADC development programs.