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.
Select Publications
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