The therapeutic antibody profiler for computational developability assessment
Abstract
The need to consider an antibody's "developability" (immunogenicity, solubility, specificity, stability, manufacturability, and storability) is now well understood in therapeutic antibody design. Predicting these properties rapidly and inexpensively is critical to industrial workflows, to avoid devoting resources to non-productive candidates. Here, we describe a high-throughput computational developability assessment tool, the Therapeutic Antibody Profiler (TAP), which assesses the physicochemical "druglikeness" of an antibody candidate. Input variable domain sequences are converted to three-dimensional structural models, and then five developability-linked molecular surface descriptors are calculated and compared to advanced-stage clinical therapeutics. Values at the extremes of/outside of the distributions seen in therapeutics imply an increased risk of developability issues. Therefore, TAP, starting only from sequence information, provides a route to rapidly identifying drug candidate antibodies that are likely to have poor developability. Our web application ( opig.stats.ox.ac.uk/webapps/tap ) profiles input antibody sequences against a continually updated reference set of clinical therapeutics.
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TAP models a set of post-Phase 1 clinical stage antibody therapeutics as distributions of five metrics thought to be linked to poor developability: total length of CDRs, extent of surface hydrophobicity, positive- and negative-charge in CDRs, and asymmetry in net heavy- and light-chain surface charges.
“For instance, therapeutic antibody profiling (TAP) models a set of post-Phase 1 clinical stage antibody therapeutics as distributions of five metrics thought to be linked to poor developability: the total length of CDRs, the extent of surface hydrophobicity, positive-and negative-charge in the CDRs, and asymmetry in the net heavy-and light-chain surface charges.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [23]
TAP provides guideline cut-offs for each metric and those cut-off values are collectively used for filtering out antibody candidates with poor developability.
“TAP also provides guideline cut-offs for each metric and those cut-off values are collectively used for filtering out antibody candidates with poor developability [23].”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [23]
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Computational and artificial intelligence-based methods for antibody development
Kim, J., McFee, M., Fang, Q., Abdin, O., & Kim, P. M. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.12.005 · Cited by 178 · see citing works
- Authors
- Raybould, M. I. J., & Deane, C. M.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Deane, Charlotte M.
- Venue
- Methods in molecular biology
- Publisher
- Springer US
- Published
- 2021-09-04
- Volume
- 2313
- Pages
- 115-125
- ISSN
- 1064-3745 (ISSN-L)
- 1940-6029
- OpenAlex
- W3197933219
- Cited by
- 25
- References
- 40
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 8 · 2023: 5 · 2021: 1
- FWCI
- 11.47
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Methods in molecular biology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- therapeutic antibodies, developability assessment, antibody design, immunogenicity, solubility, antibody structure prediction, drug-likeness, high-throughput screening, web application
- MeSH terms
- Antibodies, Antibodies, Monoclonal, Software, Solubility, Workflow
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