Explore the Agenda
8:00 am Check in & Light Breakfast
Workshop A
9:00 am Building Robust Preclinical Strategies to Validate New In Vitro Systems & Design Smarter, More Predictive Non-Clinical Packages
While first-in-human trials remain the ultimate test, this workshop focuses on how to de-risk programs earlier. It tackles the critical industry challenge of poor translation by moving beyond standard animal models to validate new in vitro systems and design smarter, more predictive non-clinical packages. Attend this workshop to improve the probability of clinical success by:
- Evaluating the predictive value of organoids, microphysiological systems, and computational models against historical clinical data for known toxicities like ILD and ocular toxicity.
- Developing a framework for reverse translation, using clinical safety signals to refine and calibrate preclinical models for better future predictions.
- Navigating the FDA’s NAMs initiative. Discussing practical strategies for implementing New Approach Methodologies and preparing robust data packages to support regulatory submissions with reduced reliance on animal studies.
12:00 pm Lunch
Workshop B
1:00 pm Leveraging AI, Computational Modeling & Pharmacogenomics to Predict ADC Toxicity & Design Safer Therapies
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.