As autoimmune T-cell engagers rapidly advance into clinical development, one of the field’s greatest challenges remains the ability to accurately predict clinical efficacy, durability, and safety before large-scale patient studies. Traditional preclinical models often fail to capture the complexity of autoimmune disease biology, creating urgent demand for more predictive translational tools, mechanistic biomarkers, and early human readouts that can de-risk development and accelerate patient benefit. This interactive session will discuss how the industry can improve translational predictability and shorten the path from discovery to clinical success by:

• Building more predictive translational models for autoimmune T-cell engagers, evaluating how advanced in vitro systems, humanized models, patient-derived assays, and mechanistic immune profiling can better predict efficacy, cytokine release syndrome risk, tissue depletion, and long-term immune reconstitution.
• Identifying the biomarkers that matter most for early clinical decision-making, discussing how pharmacodynamic biomarkers, cytokine signatures, immune-cell depletion kinetics, tissue-level readouts, and translational immune-monitoring strategies can support dose optimization, patient selection, and earlier validation of therapeutic activity
• Integrating early human data to accelerate development and reduce clinical risk, exploring how first-in-human studies, adaptive trial designs, and translational datasets can be leveraged to refine therapeutic hypotheses, improve safety management, and establish clearer pathways toward durable patient benefit and regulatory success