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Cited by 103Protein purification and stabilityMonoclonal and Polyclonal Antibodies ResearchProtein Structure and Dynamics

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Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics

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Lai, Pin-Kuang, Gallegos, Austin, Mody, Neil, Sathish, Hasige A., Trout, Bernhardt L.·2022·mAbs
DOI 10.1080/19420862.2022.2026208PMC PMC8794240
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Abstract

Machine learning has been recently used to predict therapeutic antibody aggregation rates and viscosity at high concentrations (150 mg/ml). These works focused on commercially available antibodies, which may have been optimized for stability. In this study, we measured accelerated aggregation rates at 45°C and viscosity at 150 mg/ml for 20 preclinical and clinical-stage antibodies. Features obtained from molecular dynamics simulations of the full-length antibody and sequences were used for machine learning model construction. We found a k-nearest neighbors regression model with two features, spatial positive charge map on the CDRH2 and solvent-accessible surface area of hydrophobic residues on the variable fragment, gives the best performance for predicting antibody aggregation rates (r = 0.89). For the viscosity classification model, the model with the highest accuracy is a logistic regression model with two features, spatial negative charge map on the heavy chain variable region and spatial negative charge map on the light chain variable region. The accuracy and the area under precision recall curve of the classification model from validation tests are 0.86 and 0.70, respectively. In addition, we combined data from another 27 commercial mAbs to develop a viscosity predictive model. The best model is a logistic regression model with two features, number of hydrophobic residues on the light chain variable region and net charges on the light chain variable region. The accuracy and the area under precision recall curve of the classification model are 0.85 and 0.6, respectively. The aggregation rates and viscosity models can be used to predict antibody stability to facilitate pharmaceutical development.

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Crossref · 10.1080/19420862.2022.2026208

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Figures

Figure 1. Aggregation rates of all 20 mAbs studied in this work.
Figure 1. Aggregation rates of all 20 mAbs studied in this work.CC BY-NC · PubMed Central
Figure 2. Correlation coefficients for the best two-feature linear, support vector regression (SVR) and k-nearest neighbors (KNN) regression models trained using all 20 data and LOOCV. The features for the linear regression model are SCM_neg_H2 and SASA_phobic_H3. The features for the SVR and KNN models are both SCM_pos_H2 and SASA_phobic_Fv.
Figure 2. Correlation coefficients for the best two-feature linear, support vector regression (SVR) and k-nearest neighbors (KNN) regression models trained using all 20 data and LOOCV. The features for the linear regression model are SCM_neg_H2 and SASA_phobic_H3. The features for the SVR and KNN models are both SCM_pos_H2 and SASA_phobic_Fv.CC BY-NC · PubMed Central
Figure 3. Viscosity at 150 mg/mL at pH 6.0 in histidine buffer of all 20 mAbs studied in this work. The red dashed line indicates the low/high viscosity cutoff (30 cP). A histogram showing the experimental viscosity at 150 mg/ml of 20 mAbs. The viscosity of mAb10, mAb12, mAb13, mAb14, mAb16 and mAb20 are above the high viscosity threshold 30 cP.
Figure 3. Viscosity at 150 mg/mL at pH 6.0 in histidine buffer of all 20 mAbs studied in this work. The red dashed line indicates the low/high viscosity cutoff (30 cP). A histogram showing the experimental viscosity at 150 mg/ml of 20 mAbs. The viscosity of mAb10, mAb12, mAb13, mAb14, mAb16 and mAb20 are above the high viscosity threshold 30 cP.CC BY-NC · PubMed Central
Figure 4. The relationship of viscosity at 150 mg/ml with the diffusion interaction coefficients (kD) for the 20 mAbs in this study. Open circles showing the viscosity on the y-axis and kD on the x-axis. Five high viscosity mAbs have kD values < −5 mL/g (mAb10, mAb12, mAb13, mAb16 and mAb20).
Figure 4. The relationship of viscosity at 150 mg/ml with the diffusion interaction coefficients (kD) for the 20 mAbs in this study. Open circles showing the viscosity on the y-axis and kD on the x-axis. Five high viscosity mAbs have kD values < −5 mL/g (mAb10, mAb12, mAb13, mAb16 and mAb20).CC BY-NC · PubMed Central

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Metadata

Authors
Lai, P.-K., Gallegos, A., Mody, N., Sathish, H. A., & Trout, B. L.
Affiliations
  • Stevens Institute of Technology (US)
  • AstraZeneca (United States) (US)
  • Massachusetts Institute of Technology (US)
Corresponding author
Trout, Bernhardt L.
ORCID
Venue
mAbs
Publisher
Informa UK Limited
Published
2022-01-25
Volume
14
Issue
1
Pages
2026208-2026208
ISSN
  • 1942-0862 (ISSN-L)
  • 1942-0870
DOI
10.1080/19420862.2022.2026208
PMC
PMC8794240
OpenAlex
W4207044828
Cited by
103
References
50
Citations by year
2026: 34 · 2025: 22 · 2024: 28 · 2023: 13 · 2022: 6
FWCI
5.63
Type
journal article
Language
en
Open access
Licence
Locations
  • mAbs (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
The author(s) reported there is no funding associated with the work featured in this article
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein purification and stability
  • Life Sciences › Immunology and Microbiology › Immunology › Monoclonal and Polyclonal Antibodies Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Keywords
antibody aggregation, viscosity prediction, high-concentration formulations, therapeutic proteins, monoclonal antibodies, k-nearest neighbors regression, logistic regression, hydrophobic residues, solvent accessible surface area, antibody stability, drug formulation development
MeSH terms
Machine Learning, Antibodies, Monoclonal, Humans, Viscosity, Molecular Dynamics Simulation, Protein Aggregates

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