Targeted Fcγ Receptor (FcγR)-mediated Clearance by a Biparatopic Bispecific Antibody
Bibliographic record
Abstract
Soluble ligands have commonly been targeted by antibody therapeutics for cancers and other diseases. Although monoclonal antibodies targeting such ligands can block their interactions with their cognate receptors, they can also significantly increase the half-life of their ligands by FcRn-mediated antibody recycling, thereby evading ligand renal clearance and requiring increasingly high antibody doses to neutralize the increasing pool of target. To overcome this issue, we generated a bispecific/biparatopic antibody (BiSAb) that targets two different epitopes on IL-6 to block IL-6-mediated signaling. The BiSAb formed large immune complexes with IL-6 that can bind Fcγ receptors on phagocytic cells and are rapidly internalized. In addition, rapid clearance of the BiSAb·IL-6 complex was observed in mice while the parental antibodies prolonged the serum half-life of IL-6. Intravital imaging of the liver in mice confirmed that the rapid clearance of these large immune complexes was associated with Fcγ receptor-dependent binding to Kupffer cells in the liver. The approach described here provides a general strategy for therapeutic antibodies with the ability to not only neutralize but also actively drive clearance of their soluble antigens.
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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) | 0.001 | 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 | 0.001 |
| 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".