Improving affinity and specificity of monoclonal antibodies to carbohydrate targets by computational docking and biophysical validation
Monoclonal antibodies hold the promise of selective tumor destruction in cancer therapy. Pediatric patients with metastatic neuroblastoma have been shown to benefit from therapeutic monoclonal antibodies directed at the tumor marker GD2 in minimal residual disease settings. GD2 is a complex acidic glycolipid abundantly found on the surface of neuroblastoma cells and other tumors of neuroectodermal origin, including melanoma, sarcoma, and small cell lung carcinoma. Building on the favorable clinical results of current anti-GD2 monoclonal antibodies, genetic engineering methods are available to improve their binding to both antigen and Fc receptors. Higher affinity anti-GD2 monoclonal antibodies can have substantially improved tumor-bound to free-unbound ratio and increase T-1/2 of bound antibody, ultimately translating into stronger and sustained anti-tumor effect.
Our approach is to use computational and experimental biophysical methods to guide the design of anti-GD2 monoclonal antibodies with enhanced affinity and specificity. A combination of de novo docking simulations using molecular mechanical (MM) force fields and experimental nuclear magnetic resonance (NMR) measurements are being utilized to characterize the molecular details of the antibody:antigen binding interface. In silico mutational analysis can then be used to design novel monoclonal antibodies with enhanced properties. Antibodies with substantial improvements in affinity and specificity can be further tested in therapeutic studies using neuroblastoma xenografts before further development for patient studies.
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