So-called "weak" protein-protein interactions are important for the control of solution properties and stability at elevated protein concentrations (c2) but are not practical to capture in atomistic simulations. This report focuses on a series of coarse-grained models for predicting second osmotic virial coefficients (B22) and high-concentration Rayleigh scattering (osmotic compressibility) as a function of c2 for monoclonal antibodies (MAbs) that are of interest in biotechnology. B22 and molecular volume along with c2-dependent osmotic compressibility were calculated for a series of models with increasing structural detail. Models were refined to include contributions from sterics, short-ranged van der Waals and hydrophobic attractions, screened electrostatics, and the flexibility of the mAb hinge region. The results highlight shortcomings for spherical models of MAbs and a useful balance between numerical accuracy and computational burden offered by models based on 6 or 12 spherical, partly overlapping domains. The results provide bounds for realistic values of effective charges on variable domains in order for MAbs to be stable in solution and more generally illustrate semiquantitative bounds for the space of model parameters that can reproduce experimental behavior and provide a basis for future development of computationally efficient and accurate CG mAb models to predict both low- and high-c2 behavior.

Protein-protein interactions for solutions of an IgG1 molecule were quantified using static light scattering (SLS) measurements from low to high protein concentrations (c). SLS was used to determine second osmotic virial coefficients (B) at low c, and excess Rayleigh profiles (R/K vs. c) and zero-q structure factors (S) as a function of c at higher c for a series of conditions (pH, sucrose concentration, and total ionic strength [TIS]). Repulsive (attractive) interactions were observed at low TIS (high TIS) for pH 5 and 6.5, with increasing repulsions when 5% w/w sucrose was also present. Previously developed and refined coarse-grained antibody models were used to fit model parameters from B versus TIS data. The resulting parameters from low-c conditions were used as the sole input to multiprotein Monte Carlo simulations to predict high-cR/K and S behavior up to 150 g/L. Experimental results at high-c conditions were quantitatively predicted by the simulations for the coarse-grained models that treated antibody molecules as either 6 or 12 (sub) domains, which preserved the basic shape of a monoclonal antibody. Finally, preferential accumulation of sucrose around the protein surface was identified via high-precision density measurements, which self-consistently explained the simulation and experimental SLS results.

Despite the therapeutic success of monoclonal antibodies (mAbs), early identification of developable mAb-drug candidates with optimal manufacturability, stability, and delivery attributes remains elusive. Poor solution behavior, which manifests as high solution viscosity or opalescence, profoundly affects the developability of mAb-drugs. Employing a diverse dataset of 59 mAbs, including 43 approved products, and an array of molecular descriptors spanning colloidal, conformational, charge-based, hydrodynamic, and hydrophobic properties, we show that poor solution behavior is prevalent (>30%) in mAbs and is singularly predicted (>90%) by the diffusion interaction parameter (kD), a dilute-solution measure of colloidal self-interaction. No other descriptor, individually or in combination, was found to be as effective as kD. We also show that well-behaved mAbs, a significant subset of which bear high positive charge and pI, present no disadvantages with respect to pharmacokinetics in humans. Here, we provide a systematic framework with quantitative thresholds for selecting well-behaved therapeutic mAbs during drug-discovery.

Protein interactions of α-chymotrypsinogen A (aCgn) were quantified using light scattering from low to high protein concentrations. Static light scattering (SLS) was used to determine the excess Rayleigh ratio (R) and osmotic second virial coefficients (B) as a function of pH and total ionic strength (TIS). Repulsive (attractive) protein-protein interactions (PPI) were observed at pH 5 (pH 7), with decreasing repulsions (attractions) upon increasing TIS. Simple colloidal potential of mean force models (PMF) that account for short-range nonelectrostatic attractions and screened electrostatic interactions were used to fit model parameters from data for B vs TIS at both pH values. The parameters and PMF models from low-concentration conditions were used as the sole input to transition matrix Monte Carlo simulations to predict high concentration R behavior. At conditions where PPI are repulsive to slightly attractive, experimental R data at high concentrations could be predicted quantitatively by the simulations. However, accurate predictions were challenging when PPI were strongly attractive due to strong sensitivity to changes in PMF parameter values. Additional simulations with higher-resolution coarse-grained molecular models suggest an approach to qualitatively predict cases when anisotropic surface charge distributions will lead to overall attractive PPI at low ionic strength, without assumptions regarding electrostatic "patches" or multipole expansions.

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