Machine learning feature selection for predicting high concentration therapeutic antibody aggregation
Abstract
Protein aggregation can hinder the development, safety and efficacy of therapeutic antibody-based drugs. Developing a predictive model that evaluates aggregation behaviors during early stage development is therefore desirable. Machine learning is a widely used tool to train models that predict data with different attributes. However, most machine learning techniques require more data than is typically available in antibody development. In this work, we describe a rational feature selection framework to develop accurate models with a small number of features. We applied this framework to predict aggregation behaviors of 21 approved monospecific monoclonal antibodies at high concentration (150 mg/mL), yielding a correlation coefficient of 0.71 on validation tests with only two features using a linear model. The nearest neighbors and support vector regression models further improved the performance, which have correlation coefficients of 0.86 and 0.80, respectively. This framework can be extended to train other models that predict different physical properties.
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The Trout group identified the spatial charge map (SCM) positive of the Fv region and surface accessible surface area (SASA) of the CDR-H2 and H3 as the two most important molecular dynamics simulation features in their model.
“The group identified the spatial charge map (SCM) positive of the Fv region, which describes the sum of all the positive charge patches in the variable region, and surface accessible surface area (SASA) of the CDR-H2 and H3 as the two most important molecular dynamics (MD) simulation features in their model [29].”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [29]
The Trout group proposed an ML framework obtained from various models, including linear regression, support vector regression, and nearest neighbors' regression to predict antibodies' aggregation rate, which followed the form: Aggregation rate = -0.34 × SCM positive Fv + 0.29 × SASA CDRH2H3 + 0.84.
“The Trout group proposed an ML framework obtained from various models, including linear regression, support vector regression, and nearest neighbors' regression to predict antibodies' aggregation rate, which followed the form:”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [29]
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- Authors
- Lai, P.-K., Fernando, A., Cloutier, T. K., Kingsbury, J. S., Gokarn, Y., Halloran, K. T., Calero-Rubio, C., & Trout, B. L.
- Affiliations
- Massachusetts Institute of Technology (US)
- Sanofi (United States) (US)
- Corresponding author
- Trout, Bernhardt L.
- ORCID
- Venue
- Journal of Pharmaceutical Sciences
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 110
- Issue
- 4
- Pages
- 1583-1591
- ISSN
- 0022-3549 (ISSN-L)
- OpenAlex
- W3110652321
- Cited by
- 45
- References
- 49
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 12 · 2023: 8 · 2022: 10 · 2021: 1
- FWCI
- 1.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Pharmaceutical Sciences (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Sanofi
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- feature selection, monoclonal antibodies, nearest neighbors, support vector regression, linear regression, antibody development, protein stability
- MeSH terms
- Machine Learning, Support Vector Machine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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