Development and characterization of a novel human <scp>CD137</scp> agonistic antibody with anti‐tumor activity and a good safety profile in non‐human primates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
CD137 (4-1BB, TNFRSF9), an inducible T-cell costimulatory receptor, is expressed on activated T cells, activated NK cells, Treg cells, and several innate immune cells, including DCs, monocytes, neutrophils, mast cells, and eosinophils. In animal models and clinical trials, anti-CD137 agonistic monoclonal antibodies have shown anti-tumor potential, but balancing the efficacy and toxicity of anti-CD137 agonistic monoclonal antibodies is a considerable hindrance for clinical applications. Here, we describe a novel fully human CD137 agonistic antibody (PE0116) generated from immunized harbor H2L2 human transgenic mice. PE0116 is a ligand blocker, which is also the case for Utomilumab (one of the leading CD137 agonistic drugs); PE0116 partially overlaps with Urelumab's recognized epitope. In vitro, PE0116 activates NF-κB signaling, significantly promotes T-cell proliferation, and increases cytokine secretion in the presence of cross-linking. Importantly, PE0116 possesses robust anti-tumor activity in the MC38 tumor model. In vivo, PE0116 exhibits a good safety profile and has typical pharmacokinetic characteristics of an IgG antibody in preclinical studies of non-human primates. In summary, PE0116 is a promising anti-CD137 antibody with a good safety profile in preclinical studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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) | 0.000 | 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 it