Developability assessment of engineered monoclonal antibody variants with a complex self-association behavior using complementary analytical and in silico tools.

Monoclonal antibodies (mAbs) are complex molecular structures. They are often prone to development challenges particularly at high concentrations due to undesired solution properties such as reversible self-association, high viscosity, and liquid liquid phase separation. In addition to formulation optimization, applying protein engineering can provide an alternative mitigation strategy. Protein engineering during the discovery phase can provide great benefits to optimize molecular properties, resulting in improved develop ability profiles. Here, we present a case study utilizing complementary analytical and predictive in silico methods. We have systematically identified and reengineered problematic residues responsible for the self-association of a model mAb, driven by a complex combination of hydrophobic and electrostatic interactions. Noteworthy findings include a more dominant contribution of hydrophobic interactions to self-association and potential feasibility of mutations in the CDR regions to mitigate self-association. The engineered mutation panel enabled us to assess potential correlations among commonly utilized developability screening assays, including affinity capture self-interaction nanospectroscopy (AC-SINS), dynamic light scattering (DLS), and apparent solubility by PEG-precipitation. In addition, we evaluated the correlations between experimental measurements and computational predictions. CamSol, an in silico computational tool that accounts for complex molecular interactions and neighboring hotspots, was found to be an effective screening tool. Our work led to reengineered mAb variants, better suited for high-concentration liquid formulation development. The engineered mAbs exhibited enhanced in vitro and simulated in vivo solubility and reduced self-association propensity, while maintaining binding affinity and thermal stability.