
AI & Antibodies miniseries | Reducing antibody viscosity to improve subcutaneous delivery
In Episode 3 of our AI in Antibodies mini-series, we speak to Peter Tessier, the Albert M. Mattocks Professor of Pharmaceutical Sciences and Chemical Engineering at the University of Michigan, about his groundbreaking work in the application of machine learning to predict and improve the formulation and delivery of therapeutic antibodies. Subcutaneous delivery of antibody therapeutics offers patients the convenience of at-home administration, but high-concentration formulations create viscosity challenges that limit practical delivery. Here, Peter explains how his team developed machine learning models that predict antibody viscosity and self-association, reveals surprising discoveries about formulation properties and shares practical guidance for antibody developers.Contents:[01:00] Pros and cons of subcutaneous delivery [03:30] Favorable antibody characteristics for subcutaneous delivery[05:20] Predicting viscosity[10:10] Zooming in on self-association for antibodies[15:10] Predicting sell-association based on antibody characteristics and external factors and formulation properties.[22:00] Advice for antibody therapeutics developers[24:45] Closing remarks and final requests Hosted on Acast. See acast.com/privacy for more information.










