Immunogenicity and humanization of single‐domain antibodies
Bibliographic record
Abstract
Single‐domain antibodies (sdAbs), the autonomous variable domains of camelid and shark heavy‐chain antibodies, have many desirable properties as components of biologic drugs. However, their sequences may increase the risk of immunogenicity and antidrug antibody (ADA) development in humans, and thus, sdAbs are routinely humanized during development. Here, we review and summarize the available evidence regarding the factors governing immunogenicity of sdAbs and our current state of knowledge of strategies to mitigate immunogenicity risks by humanization. While several sdAb properties, including high homology of camelid V H Hs with human IGHV3 gene products, favor low immunogenicity in humans, epitopes absent in the human repertoire including the exposed V H :V L interface may be intrinsically immunogenic. While most clinical trials have demonstrated minimal sdAb immunogenicity, two notable exceptions (the tetrameric DR5‐specific V H H TAS266 and the TNFR1‐specific V H GSK1995057) illustrate that special caution must be taken in identifying preexisting ADAs against highly potent sdAbs. Nonhuman sequence alone does not adequately explain sdAb immunogenicity, as some camelid V H Hs are nonimmunogenic while some fully human V H s elicit ADAs. The presence of preexisting ADAs directed against the exposed C‐termini of some sdAbs in a significant proportion of individuals awaits a molecular explanation. Whether sdAb humanization reduces or promotes immunogenicity remains unclear: reduction of nonhuman sequence content at the expense of introducing low‐level aggregation in humanized variants may be counterproductive. Further work will establish thresholds for V H H and V NAR humanization to maximize human sequence content while avoiding loss of binding affinity and/or immunogenicity resulting from aggregation or decreased stability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".