Antibody–Drug Conjugates (ADCs) hold tremendous promise — yet many programs struggle with resistance, toxicity, and limited clinical predictability. In this upcoming webinar, we’ll discuss how integrating AI-driven analytics, mouse clinical trial platforms, and ADC-resistant models can help de-risk ADC development and improve translational confidence from preclinical studies to clinical readout.

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.

Key Topics:

• Overcoming translational gaps in ADC development


• Leveraging AI for predictive modeling & biomarker strategy


• Using mouse clinical trials to enhance clinical relevance


• Understanding and modeling ADC resistance