Using mathematical models to set design criteria for antibody-based therapies

Mathematical models inform the design and selection of monoclonal and bispecific antibodies
Monoclonal antibodies are valuable as anticancer therapeutics because of their ability to selectively bind tumor-associated target proteins like receptor tyrosine kinases. However, most tumors are dependent on more than one pathway, which implies rational antibody combinations or bispecific antibodies. In our presentation, we will illustrate the use of kinetic computational models that capture protein–protein interactions to explore antibody combinations vs. bispecific antibodies, and different formats of bispecific antibodies, to set antibody design specifications such as affinity and valence, and to predict potency.