Covalent Immune Recruiters: Tools to Gain Chemical Control Over Immune Recognition
Bibliographic record
Abstract
Unprecedented progress made in the treatment of cancer using the body’s own immune system has encouraged the development of synthetic molecule based immunotherapeutics. An emerging class of these compounds, called Antibody Recruiting Molecules (ARMs) or Antibody Engagers (AEs), functions by reversibly binding antibodies naturally present in human serum and recruiting these to cancer cells. The recruited antibodies then engage immune cells to form quaternary complexes that drive cancer erradication. Despite their promise, the requirement to form quaternary complexes governed by multiple equilibria complicates an understanding of their in vivo efficacy. Particularly problematic are low endogenous serum antibody concentrations and rapid clearance of AEs from circulation. Here we describe a new class of trifunctional chemical tools we call covalent immune recruiters (CIRs). CIRs covalently label specific serum antibodies in a selective manner with a target protein binding ligand. CIRs thereby exert well-defined control over antibody recruitment and simplify quaternary complex equilibium, enabling probing of the resultant effects on immune recognition. We demonstrate CIRs can selectively covalently label anti-DNP IgG, a natural human antibody, directly in human serum to drive efficient immune cell recognition of targets. We expect CIRs will be useful tools to probe how quaternary complex stability impacts the immune recognition of cancer in vivo, revealing new design principles to guide the development of future AEs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".