Potential aggregation prone regions in biotherapeutics: A survey of commercial monoclonal antibodies
Open accessAbstract
Aggregation of a biotherapeutic is of significant concern and judicious process and formulation development is required to minimize aggregate levels in the final product. Aggregation of a protein in solution is driven by intrinsic and extrinsic factors. In this work we have focused on aggregation as an intrinsic property of the molecule. We have studied the sequences and Fab structures of commercial and non-commercial antibody sequences for their vulnerability towards aggregation by using sequence based computational tools to identify potential aggregation-prone motifs or regions. The mAbs in our dataset contain 2 to 8 aggregation-prone motifs per heavy and light chain pair. Some of these motifs are located in variable domains, primarily in CDRs. Most aggregation-prone motifs are rich in beta branched aliphatic and aromatic residues. Hydroxyl-containing Ser/Thr residues are also found in several aggregation-prone motifs while charged residues are rare. The motifs found in light chain CDR3 are glutamine (Q)/asparagine (N) rich. These motifs are similar to the reported aggregation promoting regions found in prion and amyloidogenic proteins that are also rich in Q/N, aliphatic and aromatic residues. The implication is that one possible mechanism for aggregation of mAbs may be through formation of cross-beta structures and fibrils. Mapping on the available Fab-receptor/antigen complex structures reveals that these motifs in CDRs might also contribute significantly towards receptor/antigen binding. Our analysis identifies the opportunity and tools for simultaneous optimization of the therapeutic protein sequence for potency and specificity while reducing vulnerability towards aggregation.
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Wang and co-workers examined molecular sequences of commercially available mAb drug products and showed that they contain multiple well defined aggregation prone motifs often located in their CDRs.
“Wang and co-workers have examined molecular sequences of commercially available mAb drug products and shown that they contain multiple well defined aggregation prone motifs often located in their CDRs [206].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [206]
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- Authors
- Wang, X., Das, T. K., Singh, S. K., & Kumar, S.
- Affiliations
- Pfizer (United States) (US)
- Corresponding author
- Kumar, Sandeep
- Venue
- mAbs
- Publisher
- Informa UK Limited
- Published
- 2009-05
- Volume
- 1
- Issue
- 3
- Pages
- 254-267
- ISSN
- 1942-0862 (ISSN-L)
- 1942-0870
- PMC
- PMC2726584
- OpenAlex
- W1597806993
- Cited by
- 198
- References
- 104
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 10 · 2023: 4 · 2022: 5 · 2021: 11 · 2020: 20 · 2019: 15 · 2018: 11 · 2017: 16 · 2016: 10 · 2015: 15 · 2014: 14 · 2013: 20 · 2012: 14
- FWCI
- 5.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- Locations
- mAbs (publishedVersion, public-domain)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Pfizer
- 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, aggregation-prone regions, monoclonal antibodies, hypervariable region, cross-β structure, fibril formation
- MeSH terms
- Amyloid, Animals, Antibodies, Monoclonal, Antigen-Antibody Complex, Humans, Immunoglobulin Fab Fragments, Immunotherapy, Prions, Protein Binding, Software, Protein Structure, Secondary, Amino Acid Motifs, Databases, Genetic, Protein Multimerization
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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