A Predictive Model for Identifying the Most Effective Anti-CCR5 Monoclonal Antibody

Abstract

CCR5 (R5) inhibition is increasingly being studied for its potential to prevent, treat, and cure illnesses. R5 is a transmembrane protein that interacts with the CD4 receptor and CXCR4 (X4) of T cells, allowing the attachment of HIV viruses to lymphocytes. Consequently, because R5 inhibition has performed well as a medicinal drug, such as maraviroc, many researchers have speculated that R5 inhibition via binding antibodies may effectively treat HIV patients. However, currently, there is a lack of information about the structural interaction between monoclonal antibodies (mAbs) and R5. The understanding of the structural CCR5 blockade via mAbs is limited. As a consequence, in this study, a predictive model with a sample size of N=160 was performed using non-linear regressions, in which the predicted reaction rates of the target R5 to gp120 interaction based on Michaelis-Menten enzyme kinetics of the inhibitor types (no, inhibitor (Control), competitive (CI), non-competitive (NI), and uncompetitive (UI)) were analyzed for their level to reduce the Vmax and Km of the R5-to-gp120 interaction.At a significant p-value of P<0.05, this study predicted that a non-competitive anti-R5 mAb would be the most effective inhibitor isotype since NI lowered the R5E Vmax to 20 μM/min with only a gp120S Km of 5 nM. A non-competitive anti-R5 mAb may more effectively inhibit the activity of CCR5, which may inform the production of more anti-R5 mAbs that are allosteric inhibitors of CCR5.