Single-Domain Antibody–Based and Linker-Free Bispecific Antibodies Targeting FcγRIII Induce Potent Antitumor Activity without Recruiting Regulatory T Cells
Bibliographic record
Abstract
Antibody-dependent cell-mediated cytotoxicity, one of the most prominent modes of action of antitumor antibodies, suffers from important limitations due to the need for optimal interactions with Fcγ receptors. In this work, we report the design of a new bispecific antibody format, compact and linker-free, based on the use of llama single-domain antibodies that are capable of circumventing most of these limitations. This bispecific antibody format was created by fusing single-domain antibodies directed against the carcinoembryonic antigen and the activating FcγRIIIa receptor to human Cκ and CH1 immunoglobulin G1 domains, acting as a natural dimerization motif. In vitro and in vivo characterization of these Fab-like bispecific molecules revealed favorable features for further development as a therapeutic molecule. They are easy to produce in Escherichia coli, very stable, and elicit potent lysis of tumor cells by human natural killer cells at picomolar concentrations. Unlike conventional antibodies, they do not engage inhibitory FcγRIIb receptor, do not compete with serum immunoglobulins G for receptor binding, and their cytotoxic activity is independent of Fc glycosylation and FcγRIIIa polymorphism. As opposed to anti-CD3 bispecific antitumor antibodies, they do not engage regulatory T cells as these latter cells do not express FcγRIII. Studies in nonobese diabetic/severe combined immunodeficient gamma mice xenografted with carcinoembryonic antigen-positive tumor cells showed that Fab-like bispecific molecules in the presence of human peripheral blood mononuclear cells significantly slow down tumor growth. This new compact, linker-free bispecific antibody format offers a promising approach for optimizing antibody-based therapies.
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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 | 0.000 | 0.000 |
| 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".