Nonnative protein aggregation is a long-standing issue during development of biopharmaceutical products. Nonnative protein aggregation has also been related to potential immunogenicity of protein-based drugs. As a result, deeper understanding on protein aggregation has become essential in the biopharmaceutical industry. Nonnative protein aggregation rates have shown strong dependence on protein concentration (c2) and solution conditions, but mass-action kinetics models have failed to explain and model this dependency. A recent hypothesis based on surface-contact probabilities showed semi-quantitative correlations with experimental aggregation rates as a function of c2. This hypothesis relies on the measurement of “weak” protein-protein interactions through the protein-protein Kirkwood-Buff integral (G22). Additionally, the c2 dependence of other physicochemical properties, such as viscosity and solubility, have also been correlated with these interactions. This presentation will focus on a methodology that combines experimental measurements at low c2 and molecular modeling to obtain protein-protein interactions at higher c2 values. Scattering techniques were used to measure structure factors (Sq), osmotic second virial coefficients (B22) and G22 of a monoclonal antibody (MAb) solution as a function of c2, pH, NaCl and other excipients concentrations. Mayer Sampling simulations of MAb-like coarse-grained models were used to compute B22 to refine against low-c2 measurements. Transition Matrix Monte Carlo in an osmotic system was used to predict G22 as a function of c2 up to 160 mg/mL of protein concentration. The results show excellent agreement with experimental Rayleigh profiles. The approach demonstrates the potential of coupling low c2 experimental measurements and molecular level simulations to accurately predict high c2 protein interactions. It also forms the basis of future work to better predict protein aggregation behavior and protein solution properties that are mediated by multi-body protein-protein interactions.

Predicting high-concentration antibody interactions with coarse-grained molecular modeling

251st National Meeting (2016)

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