Optimizing properties of antireceptor antibodies using kinetic computational models and experiments.
Monoclonal antibodies are valuable as anticancer therapeutics because of their ability to selectively bind tumor-associated target proteins like receptor tyrosine kinases. Kinetic computational models that capture protein-protein interactions using mass action kinetics are a valuable tool for understanding the binding properties of monoclonal antibodies to their targets. Insights from the models can be used to explore different formats, to set antibody design specifications such as affinity and valence, and to predict potency. Antibody binding to target is driven by both intrinsic monovalent affinity and bivalent avidity. In this chapter, we describe a combined experimental and computational method of assessing the relative importance of these effects on observed drug potency. The method, which we call virtual flow cytometry (VFC), merges experimental measurements of monovalent antibody binding kinetics and affinity curves of antibody-antigen binding into a kinetic computational model of antibody-antigen interaction. The VFC method introduces a parameter χ, the avidity factor, which characterizes the ability of an antibody to cross-link its target through bivalent binding. This simple parameterization of antibody cross-linking allows the model to successfully describe and predict antibody binding curves across a wide variety of experimental conditions, including variations in target expression level and incubation time of antibody with target. We further demonstrate how computational models of antibody binding to cells can be used to predict target inhibition potency. Importantly, we demonstrate computationally that antibodies with high ability to cross-link antigen have significant potency advantages. We also present data suggesting that the parameter χ is a physical, epitope-dependent property of an antibody, and as a result propose that determination of antibody cross-linking and avidity should be incorporated into the screening of antibody panels for therapeutic development. Overall, our results suggest that antibody cross-linking, in addition to monovalent binding affinity, is a key design parameter of antibody performance.
Merrimack Pharmaceuticals, Cambridge, Massachusetts, USA.
Brian D
BD
Harms
Jeffrey D
JD
Kearns
Stephen V
SV
Su
Neeraj
N
Kohli
Ulrik B
UB
Nielsen
Birgit
B
Schoeberl
D000911:Antibodies, Monoclonal / Q000737:chemistry / Q000235:genetics / Q000276:immunology / Q000378:metabolism*; D000915:Antibody Affinity; D000941:Antigens / Q000276:immunology / Q000378:metabolism*; D001666:Binding Sites, Antibody; D003198:Computer Simulation; D000939:Epitopes / Q000276:immunology / Q000378:metabolism; D005434:Flow Cytometry / Q000379:methods*; D006801:Humans; D007700:Kinetics; D058990:Molecular Targeted Therapy; D011485:Protein Binding; D015202:Protein Engineering / Q000379:methods*; D011956:Receptors, Cell Surface / Q000276:immunology / Q000378:metabolism*; D011994:Recombinant Proteins / Q000737:chemistry / Q000235:genetics / Q000276:immunology / Q000378:metabolism; D012107:Research Design
D000911:Antibodies, Monoclonal; D000941:Antigens; D000939:Epitopes; D011956:Receptors, Cell Surface; D011994:Recombinant Proteins