Comparative characterization of two monoclonal antibodies targeting canine PD-1
Bibliographic record
Abstract
Monoclonal antibodies targeting immune checkpoints have revolutionized oncology. Yet, the effectiveness of these treatments varies significantly among patients, and they are associated with unexpected adverse events, including hyperprogression. The murine research model used in drug development fails to recapitulate both the functional human immune system and the population heterogeneity. Hence, a novel model is urgently needed to study the consequences of immune checkpoint blockade. Dogs appear to be uniquely suited for this role. Approximately 1 in 4 companion dogs dies from cancer, yet no antibodies are commercially available for use in veterinary oncology. Here we characterize two novel antibodies that bind canine PD-1 with sub-nanomolar affinity as measured by SPR. Both antibodies block the clinically crucial PD-1/PD-L1 interaction in a competitive ELISA assay. Additionally, the antibodies were tested with a broad range of assays including Western Blot, ELISA, flow cytometry, immunofluorescence and immunohistochemistry. The antibodies appear to bind two distinct epitopes as predicted by molecular modeling and peptide phage display. Our study provides new tools for canine oncology research and a potential veterinary therapeutic.
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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 | 0.001 |
| 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".