Monoclonal antibodies (mAbs) are widely used as therapeutic agents for various diseases. High-concentration mAb formulations enable subcutaneous administration, offering greater convenience for patients. However, these formulations often suffer from elevated viscosity due to enhanced protein-protein interactions (PPIs), posing challenges for manufacturability and injectability. Excipients are commonly used to modulate solution viscosity, but their selection is often empirical and time-consuming. In this study, we employed the Site Identification by Ligand Competitive Saturation (SILCS) methodology to investigate protein-excipient interactions (PEIs) on a full-length mAb. Grand Canonical Monte Carlo/Molecular Dynamics (GCMC/MD) simulations were used to map functional group affinities across the mAb surface. Protein-protein interactions were also evaluated using a computational approach to identify residues contributing to high viscosity. Fifteen excipients were selected for computational analysis to match available experimental viscosity measurements, enabling direct comparison between in silico predictions and experimental outcomes. Residue-level PPI and PEI profiles were compared to identify potential hotspots for excipient binding. Residues exhibiting both high PPI and high PEI scores were considered key targets for viscosity modulation. Experimental viscosity measurements of the mAb at 230 mg/mL in the presence of each excipient were used to validate the computational predictions. Among the 15 excipients tested, 8 showed agreement between in silico predictions and experimental outcomes—either both reducing viscosity or both showing no effect. This integrative framework, based on SILCS simulations and experimental validation, provides a powerful approach for identifying critical interaction sites and rationally selecting excipients to reduce viscosity in high-concentration mAb formulations.
