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

Lai, Pin-Kuang; Gallegos, Austin; Mody, Neil; Sathish, Hasige A.; Trout, Bernhardt L.

doi:10.1080/19420862.2022.2026208

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

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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There are some ML models that predict aggregation propensity using antibody sequences or structures.
“Furthermore, there are some ML models that predict aggregation propensity using antibody sequences or structures [27,28].”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [27]

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Machine Learning Applied to Determine the Molecular Descriptors Responsible for the Viscosity Behavior of Concentrated Therapeutic Antibodies
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DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability
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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: Lai, Pin-Kuang
Mody, Neil
Trout, Bernhardt L.
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: gold
doi.org
Licence: cc-by-nc
creativecommons.org/licenses/by-nc/4.0/ (unspecified)
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
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › 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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Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics

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