Viscosity Prediction of High-Concentration Antibody Solutions with Atomistic Simulations

The computational prediction of the viscosity of dense
protein
solutions is highly desirable, for example, in the early development
phase of high-concentration biopharmaceutical formulations where the
material needed for experimental determination is typically limited.
Here, we use large-scale atomistic molecular dynamics (MD) simulations
with explicit solvation to de novo predict the dynamic
viscosities of solutions of a monoclonal IgG1 antibody (mAb) from
the pressure fluctuations using a Green–Kubo approach. The
viscosities at simulated mAb concentrations of 200 and 250 mg/mL are
compared to the experimental values, which we measured with rotational
rheometry. The computational viscosity of 24 mPa·s at the mAb
concentration of 250 mg/mL matches the experimental value of 23 mPa·s
obtained at a concentration of 213 mg/mL, indicating slightly different
effective concentrations (or activities) in the MD simulations and
in the experiments. This difference is assigned to a slight underestimation
of the effective mAb–mAb interactions in the simulations, leading
to a too loose dynamic mAb network that governs the viscosity. Taken
together, this study demonstrates the feasibility of all-atom MD simulations
for predicting the properties of dense mAb solutions and provides
detailed microscopic insights into the underlying molecular interactions.
At the same time, it also shows that there is room for further improvements
and highlights challenges, such as the massive sampling required for
computing collective properties of dense biomolecular solutions in
the high-viscosity regime with reasonable statistical precision.