Computational approach predicts effectiveness of bNAb combinations to treat HIV


Their study, published July 19 in eLife, suggests that the computational approach to selecting combinations of bNAbs based on genetics could help prevent viral escape and make HIV treatment more effective.

“We’ve shown the combination of PG9, PGT151, and VRC01 reduces the chance of viral rebound to less than 1%,” said Colin LaMont, PhD, a researcher at the Max Planck Institute for Dynamics and Self-Organization in Göttingen, Germany, in a statement. “It does this by targeting three different regions of the virus’ protective outer wrapping, or envelope.”

The researchers contend that their computational approach, which includes high-throughput sequencing to analyze the genetics of HIV viruses, may also be useful for designing therapies against other rapidly evolving agents that cause disease, such as the hepatitis C virus, drug-resistant bacteria, and cancer tumor cells.

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