bioRxivBiophysics
Viscosity Prediction of High-ConcentrationAntibody Solutions with AtomisticSimulations
Abstract
The computational prediction of the viscosity of dense protein solutions is highly desirable, for example in the early development phase of high-concentration biophar-maceutical formulations where the material needed for experimental determination is typically limited. Here, we use large-scale atomistic molecular dynamics (MD) simulations with explicit solvent 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 mg/ml and 250 mg/ml are compared to the experimental values, which we measured with rotational rheometry. The computational viscosity of 24 mPa s at a 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, the present 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. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/555069v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@4dd23corg.highwire.dtl.DTLVardef@12579eaorg.highwire.dtl.DTLVardef@167151dorg.highwire.dtl.DTLVardef@9b3306_HPS_FORMAT_FIGEXP M_FIG C_FIG
- assay: long-read sequencing
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
- approach: simulation
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Predicting the Dynamic Viscosity of High-Concentration Antibody Solutions with a Chemically Specific Coarse-Grained Model
Tobias M. Prass, Patrick Garidel, … Lars V. Schäfer·The Journal of Physical Chemistry LettersPeer-reviewed·Dec 7, 2025
- Structure-Based Calculation of Excipient Effects on the Viscosity ofConcentrated Antibody Solutions
John C. Shelley, Qing Chai, … Jonathan Jou·mAbsPeer-reviewed·Mar 21, 2026
- Enhancing viscosity control in antibody formulations: A framework for the biophysical screening of mutations targeting solvent-accessible hydrophobic and electrostatic patches
G. B. Armstrong, V. Shah, … Z. Rattray·bioRxiv·Mar 7, 2024
- Self-Interaction Nanoparticle Spectroscopy Predicts High-Concentration Viscosity of Therapeutic IgG1 Antibodies
Santosh Kumar Paidi, Julissa Ibrahim, … Tomasz Baginski·mAbsPeer-reviewed·Jul 30, 2026
- Temperature and Excipient Mediated Modulation of Monoclonal Antibody Interactions Revealed by kD, Rheology, and Raman Spectroscopy
Olivia Noelle Eskens, Sabitoj Singh Virk, … Samiul Amin·mAbsPeer-reviewed·Jun 25, 2025
- Measurement intensification for antibody formulations: combined measurement of protein size, interactions, and viscosity by differential dynamic microscopy
Caidric Indaya Gupit, Anoushka Shandilya, … Matthew E. Helgeson·bioRxiv·Jan 21, 2026