Molecular Determinants of Inverse Agonist Activity of Biologicals Targeting CTLA-4
Bibliographic record
Abstract
Ligation of CD28 or CTLA-4 with some biologicals can activate T cells due to an unexpected superagonist or inverse agonist activity, respectively. The risk of such an outcome limits the therapeutic development of these reagents. Thus, identifying the molecular determinants of superagonist/inverse agonist properties for biologicals targeting costimulatory/inhibitory receptors has not only fundamental value but also important therapeutic implications. In this study, we show that ligation of CTLA-4 with either soluble B7.1 Ig (but not B7.2 Ig) or with a recombinant bispecific in-tandem single chain Fv known as 24:26 induces TCR-independent, T cell activation. Such an inverse agonist activity requires CD28 expression and high CTLA-4 expression and is not seen when CTLA-4 is ligated by membrane-bound B7.1 or B7.2. At the molecular level, the inverse agonist activity of B7.1 Ig or 24:26 correlates with their ability to induce the formation of unique dimer-based, CTLA-4 oligomers on the T cell surface and involves CTLA-4 signaling through its cytoplasmic domain. Our results provide a potential mechanism to explain and to predict inverse agonist activity for CTLA-4 ligands.
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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 | 0.001 |
| 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".