This presentation is being submitted as one piece of a larger, two-hour symposium that would be chaired by John M. Burke, PhD, Co-Founder, President and CEO, Applied BioMath. Bi-specific antibodies are an attractive modality to modulate multiple targets in a disease indication. Each antigen may exhibit similar or different kinetic values like half-life, internalization rates, and expression rates. Target coverage for each antigen may also differ or be similar. Understanding your drug targets is critical to building an appropriate drug that specifically binds to and elicits the magnitude and duration of response needed for a particular indication. In this presentation, a case study will be presented about a tiered model-based approach to first determine feasibility of a bi-specific antibody to appropriately cover multiple antigen pairs. Once feasibility was assessed, further modeling was performed to determine ideal affinity ranges for each target in bi-specific format at the site of action. Sensitivity analysis was performed to understand each parameter and its impact on predicted target coverage. This approach guided teams for informed antibody design, prioritization of experiments, and triaging of challenging antigen pairs.
Upon completion, participants will understand the value of using math modeling to assess the feasibility of engaging two targets within preset proposed therapeutic parameters.
Upon completion, participants will learn how leveraging math modeling helps influence the design of a bispecific therapeutic.
Upon completion, participants will see an example of using math modeling for early feasibility assessment to pick the ideal second targeting antigen for a therapeutic.