Abstract

Ovarian cancer is one of the most lethal gynecologic cancers. The high mortality rate is due to lack of early symptoms and developing drug resistance. Immunotherapy has emerged as a promising approach to treatment of cancer. Bevacizumab is a recombinant humanized monoclonal IgG antibody against VEGF A that used in cancer immunotherapy, especially in ovarian cancer therapy. For a successful cancer immunotherapy the different features of therapeutic antibodies such as binding affinity should be improved. Increased affinity enhances the biological action of the antibody, which in turn improves the therapeutic effects. Furthermore, the increased antibody affinity can reduce the therapeutic dose of the antibody, resulting in lower toxicity and handling cost. Considering the importance of this issue, using in silico analysis, we aimed to find the important amino acids of the Bevacizumab antibody, and then replaced these amino acids with others to improve antibody binding affinity. Finally, we examined the binding affinity of antibody variants to antigens. In conclusion, the designed antibodies could be potential candidates for binding to antigens with improved affinity.