Coarse-Grained Molecular Dynamics Simulations for Understanding the Impact of Short-Range Anisotropic Attractions on Structure and Viscosity of Concentrated Monoclonal Antibody Solutions

Abstract

Understanding protein–protein interactions in concentrated therapeutic monoclonal antibody (mAb) solutions is desirable for improved drug discovery, processing, and administration. Here, we deduce both the net protein charge and the magnitude and geometry of short-ranged, anisotropic attractions of a mAb across multiple concentrations and cosolute conditions by comparing structure factors S(q) obtained from small-angle X-ray scattering experiments with those from molecular dynamics (MD) simulations. The simulations, which utilize coarse-grained 12-bead models exhibiting a uniform van der Waals attraction, uniform electrostatic repulsion, and short-range attractions between specific beads, are versatile enough to fit S(q) of a wide range of protein concentrations and ionic strength with the same charge on each bead and a single anisotropic short-range attraction strength. Cluster size distributions (CSDs) obtained from best fit simulations reveal that the experimental structure is consistent with small reversible oligomers in even low viscosity systems and help quantify the impact of these clusters on viscosity. The ability to systematically use experimental S(q) data together with MD simulations to discriminate between different possible protein–protein interactions, as well as to predict viscosities from protein CSDs, is beneficial for designing mAbs and developing formulation strategies that avoid high viscosities and aggregation at high concentration.

Computational models for studying physical instabilities in high concentration biotherapeutic formulations

“…The resulting CG model was used for calculating the cluster size distribution of the solution, which in turn was used to reproduce the solution viscosity via an empirical equation. 88 This approach predicted reasonably well the changes in viscosity with respect to protein concentrations for both mAbs in most of the tested formulations, as well as for a polyclonal IgG; 198 however, it failed to capture these changes when protein clustering is driven by strong anisotropic interactions. More recently, Izadi et al .…”

“…As a consequence, it is often observed that these models cannot be transferred between different protein systems, or they are even unable to predict the behavior of the same system at different thermodynamic states. 82 , 87 , 88 Finally, most CG models are developed in an implicit-solvent framework, where the behavior of the solvent and any excipient in solution is averaged out and absorbed in the potential energy function for protein–protein interactions. If one were interested in studying the effects of excipients on the stability of protein solutions (e.g., during formulation screening studies), a different set of CG force-fields for each combination of excipients would be required to carry out such in-silico studies.…”

“… 85 and Chowdhury et al . 88 used a similar 12-CG-site mAb model in combination with small-angle X-ray scattering (SAXS) experiments to predict the concentration-dependent viscosity of two mAbs over a broad range of formulations. SAXS data was used to determine whether anisotropic protein interactions were relevant at a given solution condition, as well as to parameterize the CG force field.…”

“… 85 and Chowdhury et al . 88 , 198 used a 12 CG-site model to evaluate experimental from two different mAbs and a bovine immunoglobulin in various formulation conditions, where the CG-sites interact through a weak short-range attraction and an electrostatic repulsion. Although these reports show that the 12 CG-site model is able to reasonably reproduce the regions of related to the bulk behavior and nearest-neighbor shell (i.e., the low- and intermediate- regions), the addition of a strong attractive potential to the outer CG-sites of the model is required for fitting the data from net attractive formulations.…”

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

“… 241–246 Although MD simulation-based parameters, such as short-range interactions, van der Waals attractions and electrostatic repulsions are also used to develop models to predict viscosity of antibodies under a wide range of concentration and ionic strength. 247 , 294 A mutagenesis study using MD simulations and experiments showed that replacing surface-exposed aromatic AA residues reduces the viscosity of antibodies. 248 Schwenger et al 249 measured the viscosity as a function of concentration using Ross-Minton model and temperature using the Arrhenius equation and tested it on four mAbs in the range of potential clinical formulation.…”