Multiscale Coarse-Grained Approach to Investigate Self-Association of Antibodies

Area of Science:

• Biophysics
• Computational Chemistry
• Pharmaceutical Sciences

Background:

• High viscosity in concentrated therapeutic monoclonal antibody (mAb) solutions is a significant formulation challenge.
• Predicting mAb self-association early in drug development is crucial due to limited material availability.

Purpose of the Study:

• To develop and validate a coarse-grained (CG) simulation method for predicting the self-association and viscosity of full-length antibodies at high concentrations.
• To enable material-free screening of antibody candidates for desirable formulation properties.

Main Methods:

• Developed a novel CG simulation approach incorporating accurate electrostatic and hydrophobic interactions for full-length antibodies.
• Performed microsecond molecular dynamics simulations on 15 different mAbs at high concentrations.
• Validated simulation-derived diffusion coefficients against experimentally measured viscosities.

Main Results:

• CG simulations accurately predicted diffusion coefficients influenced by intermolecular interactions in crowded antibody environments.
• Simulation results correlated well with experimental viscosity measurements.
• Electrostatic and hydrophobic properties effectively predicted salt's effect on mAb solution viscosity.

Conclusions:

• The presented CG modeling approach offers a powerful, material-free tool for screening antibody candidates.
• Accurate prediction of viscosity properties early in development can streamline the selection of promising therapeutic mAbs.