At the ADC Summit 2026, stakeholders across the biopharmaceutical and oncology ecosystem will examine how a proactive toxicity strategy enables smarter decision processes across discovery, preclinical, and clinical development. Instead of responding to adverse events late in clinical stages, leading teams are integrating toxicity considerations early, connecting mechanistic understanding, translational biomarkers, and exposure-response learning to guide molecule selection, dosing strategies, and trial design. This approach allows organizations to minimize attrition, preserve asset value, and advance differentiated ADCs with greater predictability.
Predictive Toxicity as a Driver of Clinical Confidence
One of the most influential advancements in ADC development is the integration of improved preclinical models and translational biomarkers to anticipate toxicity before first-in-human studies. Traditional toxicology approaches often fail to capture payload-driven effects, bystander toxicity, and off-tumor target engagement that appear in clinical settings. In contrast, modern toxicity strategies emphasize human-relevant models, biomarker-guided risk evaluation, and early exposure-response characterization.
These approaches enable development teams to identify dose-limiting toxicities earlier, refine therapeutic windows, and design clinical protocols with integrated mitigation strategies. Prophylaxis planning, patient monitoring frameworks, and adaptive dosing regimens are now guided by preclinical-to-clinical translation rather than trial-and-error escalation. For sponsors and partners, this results in fewer late-stage setbacks, more resilient development programs, and stronger alignment with regulators who prioritize patient safety alongside innovations.
Optimizing ADC Platforms for Long-Term Value
As the ADC landscape grows more competitive, toxicity management is emerging as a key differentiator between platforms that succeed and those that struggle. Payload class selection, linker stability, and conjugation chemistry all influence systemic exposure, tissue distribution, and cumulative toxicity. A structured toxicity strategy enables organizations to develop institutional knowledge around payload-specific risk profiles and apply those insights across pipelines.
Model-informed drug development is playing an expanding role in supporting regimen optimization, combination strategies, and lifecycle planning. By leveraging exposure-response insights, teams can justify alternative dosing schedules, support label expansion, and safely evaluate new indications. For CDMOs, CROs, and enabling technology providers, this presents a significant opportunity to assist sponsors with integrated solutions spanning analytics, toxicology, clinical pharmacology, and operational execution.
