Integrating In Silico and In Vitro Tools for Optimized Antibody Development-Design of Therapeutic Anti-oxMIF...

Area of Science:

• Biotechnology and Pharmaceutical Sciences
• Protein Engineering and Antibody Development

Background:

• Antibody developability assessment is critical for optimizing drug candidates.
• Predictive tools for protein structure and properties aid biologic development.
• Accurately predicting amino acid substitution effects on antibody structure, especially CDR3s, remains a challenge.

Purpose of the Study:

• To engineer improved antibodies targeting oxidized macrophage migration inhibitory factor (oxMIF).
• To address liabilities in the first-generation anti-oxMIF antibody, imalumab, by reducing aggregation and self-interaction.

Main Methods:

• Combined in silico predictive tools with in vitro experimental validation.
• Utilized structural prediction and molecular dynamics simulations to guide mutation selection.
• Engineered mutations in variable regions of imalumab to improve biophysical properties.

Main Results:

• Identified hydrophobic hotspots in imalumab responsible for aggregation and short half-life.
• Developed a lead candidate antibody, C0083, with reduced hydrophobicity and self-interaction via heavy chain CDR3 loop restructuring.
• C0083 maintained target specificity and binding affinity to oxMIF despite structural modifications.

Conclusions:

• A small number of precisely selected mutations can significantly enhance antibody biophysical properties.
• This approach successfully improved the developability profile of the anti-oxMIF antibody.