Main Conference Day 1Monday, 14 December 2026 - PT (Pacific Time, GMT-08:00)

Novel genetic models allow us to harness and manipulate the germinal center’s Darwinian processes. Using transgenic murine models, we can enhance the breadth and potency of known monoclonal antibodies (mAbs), isolate previously unobserved highly potent antibodies, and characterize the features correlated with antibody efficacy to create entirely novel therapeutics and prophylactics.

The complex mechanism of action of antibody-drug conjugates (ADCs) requires comprehensive translational strategies to identify biomarkers predictive of response. The entirety of the ADC mechanism(s) of action should be explored: target and biology, mAb and payload. Incorporating robust translational research early in the drug development process is key for successful transition from the laboratory to a therapy benefiting patients and to guide innovation of the next generation of ADCs.

Conventional therapy for autoimmunity is broad immunosuppression, which includes adverse effects and does not alter the underlying cause of disease. Antibody therapies can target dysregulated biological pathways and cell functions while avoiding broad immunosuppression. Early success in efficacy came with toxicity. However, advances in Fc-engineering and successes in oncology propelled further development of antibodies for autoimmunity. The past, present, and future of antibody-based therapeutics in autoimmune diseases will be explored.

In this presentation, I will highlight recent advances in the development of recombinant snakebite antivenoms and show how display technologies enable the discovery of broadly neutralizing monoclonal antibodies and nanobodies with superior efficacy in rodent envenoming models compared to conventional plasma-derived antivenoms. I will also discuss the key scientific, technical, and translational challenges that must be addressed to bring recombinant antivenoms into clinical use.

G protein-coupled receptors (GPCRs) are the largest class of membrane proteins in humans and serve as key drug targets for a wide variety of indications. Using a combination of computational and experimental screening methods we have developed agonistic and antagonistic VHH antibody fragments targeting GPCRs for potential therapeutic uses. These tools can also serve as the basis for bispecifics that can induce receptor degradation or control subcellular localization, enabling new and distinctive pharmacological activities compared to small-molecule ligands.

While a vast number of antibody sequences have been reported in the literature, the majority remain uncharacterized. For instance, among over 5,000 influenza antibodies discovered to date, only 20% have defined epitope information. To bridge this gap, we have developed machine learning models and high-throughput experimental screening to analyze the epitopes and binding specificity of these uncharacterized antibodies. Our findings provide novel molecular insights into the landscape of influenza antibodies.

Agonistic antibodies targeting co-stimulatory receptors represent a promising strategy for cancer immunotherapy. Using structure-guided disulfide engineering, we generated conformationally-restricted antibodies with enhanced agonism, by introducing non-native disulfides between F(ab) arms. An integrative approach combining structural, computational and cellular methods was used to validate and characterise our designs. These findings establish conformational tuning of immunostimulatory antibodies as a readily translatable strategy for the rational development of more effective anti-cancer therapeutics.

AI-generated antibodies raise new questions for patent drafting, inventorship, enablement, written description, and obviousness. This presentation explores how to build antibody patent portfolios around sequence differences, CDR modifications, epitope characterization, functional advantages, and experimental data that support protection in a changing legal landscape.