Calculation of therapeutic antibody viscosity with coarse-grained models, hydrodynamic calculations and machine learning-based parameters

Abstract

High viscosity presents a challenge for manufacturing and drug delivery of therapeutic antibodies. The viscosity is determined by protein–protein interactions among many antibodies. Molecular simulation is a promising method to study protein–protein interactions; however, all-atom models do not allow the simulation of multiple molecules, which is necessary to compute viscosity directly. Coarse-grained models, on the other hand can do this. In this work, a 12-bead coarse-grained model based on Swan and coworkers (J. Phys. Chem. B 2018, 122, 2867–2880) was applied to study antibody interactions. Two adjustable parameters related to the short-range interactions on the variable and constant regions were determined by fitting experimental data of 20 IgG1 monoclonal antibodies at 150 mg/mL. The root-mean-square deviation improved from 1 to 0.68, and the correlation coefficient improved from 0.63 to 0.87 compared to that of a previous model that assumed the short-range interactions were the same for all the beads. Our model is also able to calculate the viscosity over a wide range of concentrations without additional parameters. A tabulated viscosity based on our model is provided to facilitate antibody screening in early-stage design.

Differences in human IgG1 and IgG4 S228P monoclonal antibodies viscosity and self-interactions: Experimental assessment and computational predictions of domain interactions

“…In addition to low and high viscosity classifiers, we also implemented a recently developed CG model combined with hydrodynamic calculations to calculate viscosity. 27 This model was developed and validated based on 20 IgG1 mAbs up to 150 mg/ml in 10 mM histidine-HCl at pH 6.0. 27 We attempted to validate and extend this model to the IgG4P isotype.…”

“… 27 This model was developed and validated based on 20 IgG1 mAbs up to 150 mg/ml in 10 mM histidine-HCl at pH 6.0. 27 We attempted to validate and extend this model to the IgG4P isotype. Figure 6 illustrates the concentration dependence of the viscosity from CG models with the experimental viscosity data for IgG1 and IgG4P mAbs extrapolated to zero shear rate.…”

“…Previous publications have described that head-to-head interactions lead to extended linear clusters, while head-to-tail interactions lead to compact and branched clusters. 27 , 34 Given the same interaction energies, the extended cluster would have higher viscosity than that of branched clusters. 34 Furthermore, IgG4P mAbs that exhibit lower net charge could lead to enhanced self-association.…”

“…The CG model was described elsewhere. 27 Briefly, a 12-bead CG model derived from all-atom MD simulations was used to construct the structure of all the mAbs. The intramolecular interaction parameters were obtained from the dynamic averages of the MD simulations.…”

“…All 14 IgG4P mAbs in our study showed an increase in self-interaction as compared to their corresponding IgG1 mAbs. Three computational models, namely, spatial charge map (SCM), 25 a decision tree model previously described, 26 and a coarse-grained (CG) model, 27 were applied in an attempt to predict self-interaction and viscosity behaviors of these IgG1/IgG4 pairs. The microstructure of clusters by the radial distribution function was also analyzed to gain insight into domain interactions.…”