As ADC pipelines grow more complex, traditional toxicology approaches alone are no longer sufficient to anticipate the multifactorial drivers of toxicity. This workshop explores how artificial intelligence, computational toxicology, and pharmacogenomic insights can be integrated to predict toxicity risks earlier and guide the design of safer ADCs. By combining multi-omic datasets with predictive modeling, attendees will learn how to identify patient-specific risk factors, anticipate organ-specific toxicities, and refine candidate selection before clinical development. Attend this workshop to harness data driven approaches for safer ADC development by:

• Applying AI to identify toxicity risk patterns, using machine learning trained on preclinical and clinical datasets to link payload, linker, and antibody properties with organ-specific toxicities.
• Building computational models to predict ADC toxicity and integrate PK, biodistribution, and payload-release data into PBPK and systems pharmacology models to forecast exposure and dose-limiting toxicities.
• Leveraging pharmacogenomics to uncover patient risk factors to identify genetic variants affecting Fc-receptors, immune pathways, and drug transporters that influence ADC clearance and toxicity susceptibility.