Antitumor efficacy and reduced toxicity using an anti-CD137 Probody therapeutic
Bibliographic record
Abstract
Costimulation via CD137 (4-1BB) enhances antitumor immunity mediated by cytotoxic T lymphocytes. Anti-CD137 agonist antibodies elicit mild liver inflammation in mice, and the maximum tolerated dose of Urelumab, an anti-human CD137 agonist monoclonal antibody, in the clinic was defined by liver inflammation-related side effects. A protease-activated prodrug form of the anti-mouse CD137 agonist antibody 1D8 (1D8 Probody therapeutic, Pb-Tx) was constructed and found to be selectively activated in the tumor microenvironment. This construct, which encompasses a protease-cleavable linker holding in place a peptide that masks the antigen binding site, exerted antitumor effects comparable to the unmodified antibody but did not result in liver inflammation. Moreover, it efficaciously synergized with both PD-1 blockade and adoptive T-cell therapy. Surprisingly, minimal active Pb-Tx reached tumor-draining lymph nodes, and regional lymphadenectomy did not abrogate antitumor efficacy. By contrast, S1P receptor-dependent recirculation of T cells was absolutely required for efficacy. The preferential cleavage of the anti-CD137 Pb-Tx by tumor proteases offers multiple therapeutic opportunities, including neoadjuvant therapy, as shown by experiments in which the Pb-Tx is given prior to surgery to avoid spontaneous metastases.
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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) | 0.001 | 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".