ArticlemAbs
Calculation of therapeutic antibody viscosity with coarse-grained models, hydrodynamic calculations and machine learning-based parameters.
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Who cites it
25 citing papers in PubMed.
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- Fab-Fc and Fab-Fab interactions of variable strength and valency contribute to the high concentration viscosity of IgGProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Review
- Viscosity of concentrated antibodies from a dynamic model of electrostatics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Electrostatics and viscosity are strongly linked in concentrated antibody solutions.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability.Computational and structural biotechnology journal · 2024Article
- A framework for the biophysical screening of antibody mutations targeting solvent-accessible hydrophobic and electrostatic patches for enhanced viscosity profiles.Computational and structural biotechnology journal · 2024Article
- A First Insight into the Developability of an Immunoglobulin G3: A Combined Computational and Experimental Approach.ACS pharmacology & translational science · 2024Article
- Sequence-Based Viscosity Prediction for Rapid Antibody Engineering.Biomolecules · 2024Article
- Using Cluster Theory to Calculate the Experimental Structure Factors of Antibody Solutions.Molecular pharmaceutics · 2023Article
- IgG1 and IgG4 antibodies sample initial structure dependent local conformational states and exhibit non-identical Fab dynamics.Scientific reports · 2023Article
- Accelerating therapeutic protein design with computational approaches toward the clinical stage.Computational and structural biotechnology journal · 2023Review
- DeepSCM: An efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity.Computational and structural biotechnology journal · 2022Article
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- Review
- Review
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Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.