2026 Short Courses

Antibody-drug conjugates are now an established modality, but the field is changing far faster than simple iteration. This workshop charts that evolution across six connected themes.We start with the payload revolution: why approved ADCs rely on a few cytotoxic mechanisms, and how degraders, immune agonists, cytokines and nucleic acids change what a conjugate can carry, with cytosolic delivery as the key bottleneck. We then examine antibody-oligonucleotide and siRNA conjugates, including the first regulatory filings and the gap between molecular and clinical endpoints. On targeting, we review bispecific and multispecific designs, using the first approved bispecific ADC as a case study. We then move beyond oncology to autoimmune and inflammatory disease, where non-cytotoxic payloads and chronic dosing redefine benefit-risk. We explore target expansion into stromal, vascular and checkpoint-family antigens, and close with lessons from recent clinical successes and failures: therapeutic window, dose, endpoints, patient selection and delivery.

Format: short polls, case-based discussion and a “spot the failure” exercise. Participants leave with a framework to assess any next-generation conjugate on payload mechanism, delivery bottleneck, targeting logic, disease biology and clinical precedent.

Topics to be discussed include:

• Which non-classical payload class (degraders, immune agonists, cytokines, oligonucleotides) will reach approval first, and what must change in delivery?
• When does a bispecific conjugate beat two separate ADCs?
• Can conjugates compete with oral drugs and biologics in autoimmune disease?
• Would you advance a stromal antigen without human efficacy data?
• Which failure mode is most under-appreciated in your own program?

Topics to be covered:

• Introduction to bispecifics and bispecific formats 
• Therapeutic applications of bispecific antibodies 
• Developability of bispecifics 
• Case study: discovery and development of an FDA-approved bispecific antibody​​

Topics to be covered include: 

• Overview of sequence, structure-guided, ML (machine learning) tools for developability and designs 
• Overview and demo of various ML tools from Oxford Protein Informatics Group (OPIG)
• Antibody specific language models (Ablang – Olsen et al 2022, Ablang2 – Olsen et al 2024)
• Antibody (and nanobody) structure prediction (ABodyBuilder2) Abanades et al 2023) 
• Therapeutic antibody profiling and developability evaluation (TAP – Raybould et al 2019, TAP2 – Raybould et al 2024) 
•  Antibody sequence optimization with inverse folding (AntiFold – Hummer et al 2023)
• ​In silico developability assessment—case studies​​