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

Lai, Pin-Kuang; Ghag, Gaurav; Yu, Yao; Juan, Veronica; Fayadat‐Dilman, Laurence; Trout, Bernhardt L.

doi:10.1080/19420862.2021.1991256

Cited by 16 publications

(33 citation statements)

References 46 publications

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“…Radial distribution functions accounting only for beads that participate in specific short-range attractions, g SRA ( r ), are more sensitive to interaction changes. , At low Q (Figure c), attractions cause a sharp peak to form at r ∼ 1 d B with a magnitude that increases with K , indicating association between mAb arms. For K = 2.5 k B T , an additional small peak is seen at r ∼ 2 d B corresponding to two or more mAb arms interacting at the same site, consistent with denser aggregates.…”

Section: Resultsmentioning

confidence: 99%

Characterizing Experimental Monoclonal Antibody Interactions and Clustering Using a Coarse-Grained Simulation Library and a Viscosity Model

et al. 2023

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Attractive protein−protein interactions in concentrated monoclonal antibody (mAb) solutions may lead to the formation of clusters that increase viscosity. Here, we propose an analytical model that relates mAb solution viscosity to clustering by accounting for the contributions of suboptimal mAb packing within a cluster and cluster fractal dimension. The influence of short-range, anisotropic attractions and longrange Coulombic repulsion on cluster properties is investigated by analyzing the clustersize distributions, cluster fractal dimensions, radial distribution functions, and static structure factors from a library of coarse-grained molecular dynamics simulations. The library spans a vast range of mAb charges and attractive interactions in solutions of varying ionic strength. We present a framework for combining the viscosity model and simulation library to successfully characterize the attraction, repulsion, and clustering of an experimental mAb in three different pH and cosolute conditions by fitting the measured viscosity or structure factor from small-angle X-ray scattering. At low ionic strength, the cluster-size distribution is impacted by strong charges, and both the viscosity and net charge or structure factor and net charge must be considered to deconvolute the effects of short-range attraction and long-range repulsion.

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Section: Resultsmentioning

confidence: 99%

Characterizing Experimental Monoclonal Antibody Interactions and Clustering Using a Coarse-Grained Simulation Library and a Viscosity Model

et al. 2023

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“…We investigate this mechanism in this work, using all-atom Molecular Dynamics (MD) simulations. Hence, our work significantly aligns with the aspirations of the community to identify low-cost approaches that can assist drug development, as reflected in recent works. − Here we quantify the interactions between mAb COE3 (and its Fab and Fc fragments) and histidine in aqueous solution and identify the preferred sites for histidine adsorption on COE3. We introduce the BSAP index, which is an extension of the Structural Aggregation Propensity (SAP) metric introduced by Chennamsetty et al .…”

Section: Introductionmentioning

confidence: 99%

Understanding the Stabilizing Effect of Histidine on mAb Aggregation: A Molecular Dynamics Study

Saurabh

Kalonia

et al. 2022

Mol. Pharmaceutics

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Histidine, a widely used buffer in monoclonal antibody (mAb) formulations, is known to reduce antibody aggregation. While experimental studies suggest a nonelectrostatic, nonstructural (relating to secondary structure preservation) origin of the phenomenon, the underlying microscopic mechanism behind the histidine action is still unknown. Understanding this mechanism will help evaluate and predict the stabilizing effect of this buffer under different experimental conditions and for different mAbs. We have used all-atom molecular dynamics simulations and contact-based free energy calculations to investigate molecular-level interactions between the histidine buffer and mAbs, which lead to the observed stability of therapeutic formulations in the presence of histidine. We reformulate the Spatial Aggregation Propensity index by including the buffer–protein interactions. The buffer adsorption on the protein surface leads to lower exposure of the hydrophobic regions to water. Our analysis indicates that the mechanism behind the stabilizing action of histidine is connected to the shielding of the solvent-exposed hydrophobic regions on the protein surface by the buffer molecules.

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“…The 61 mAbs viscosity data at 150 mg/mL were from our previous works. [7][8][9] The first 20 data were preclinical…”

Section: Comparison Of the Viscosity Prediction Models For Preclinica...mentioning

confidence: 99%

“…The calculation of the SCM score requires MD simulations. The SCM model was compared with the machine learning model for the 14 IgG1/14 IgG4P commercial mAbs 8 and 20 preclinical/clinical mAbs 9 mentioned earlier to evaluate the prediction accuracy. The accuracy for the 14 IgG1 and 14 IgG4 commercial mAbs were 0.93 and 0.79, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…6 Recently, machine learning has been applied to predict high concentration antibody viscosity. [7][8][9] Because of limited experimental data, only conventional machine learning algorithms such as logistic regression, support vector classification, and decision tree classification were applied. The machine learning features relied on domain expertise and published literature.…”

Section: Introductionmentioning

confidence: 99%

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DeepSCM: an efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity

Lai

2022

Preprint

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Predicting high concentration antibody viscosity is essential for developing subcutaneous administration. Computer simulations provide promising tools to reach this aim. One such model is the spatial charge map (SCM) proposed by Agrawal and coworkers (mAbs. 2015, 8(1):43–48). SCM applies molecular dynamics simulations to calculate a score for the screening of antibody viscosity at high concentrations. However, molecular dynamics simulations are computationally costly and require structural information, a significant application bottleneck. In this work, high throughput computing was performed to calculate the SCM scores for 6596 nonredundant antibody variable regions. A convolutional neural network surrogate model, DeepSCM, requiring only sequence information, was then developed based on this dataset. The linear correlation coefficient of the DeepSCM and SCM scores achieved 0.9 on the test set (N=1320). The DeepSCM model was applied to screen the viscosity of 38 therapeutic antibodies that SCM correctly classified and resulted in only one misclassification. The DeepSCM model will facilitate high concentration antibody viscosity screening. The code and parameters are freely available at https://github.com/Lailabcode/DeepSCM.

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

Cited by 16 publications

References 46 publications

Characterizing Experimental Monoclonal Antibody Interactions and Clustering Using a Coarse-Grained Simulation Library and a Viscosity Model

Characterizing Experimental Monoclonal Antibody Interactions and Clustering Using a Coarse-Grained Simulation Library and a Viscosity Model

Understanding the Stabilizing Effect of Histidine on mAb Aggregation: A Molecular Dynamics Study

DeepSCM: an efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity

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