Maplirpacept: a CD47 decoy receptor with minimal red blood cell binding and robust anti-tumor efficacy
Bibliographic record
Abstract
Introduction CD47 is highly expressed on cancer cells and triggers an anti-phagocytic “don’t eat me” signal when bound by the inhibitory signal regulatory protein α (SIRPα) expressed on macrophages. While CD47 blockade can mitigate tumor growth, many CD47 blockers also bind to red blood cells (RBCs), leading to anemia. Maplirpacept (TTI-622, PF-07901801) is a CD47 blocking fusion protein consisting of a human SIRPα fused to an IgG4 Fc region and designed to limit binding to RBCs. Methods To determine maplirpacept binding to RBCs and interference with blood tests, human blood samples were used. The ability of maplirpacept to promote macrophage-mediated phagocytosis of human tumor cells was assessed using both confocal microscopy and flow cytometry. In vivo antitumor efficacy as a monotherapy and in combination with other therapeutic agents was evaluated in xenograft models. Results In the current study, we demonstrate that maplirpacept has limited binding to RBCs while driving enhanced macrophage-mediated phagocytosis of hematological tumor cells in vitro and reducing tumor burden in human xenograft models. Moreover, phagocytosis of neoplastic cells can be enhanced when maplirpacept is combined with other therapeutic agents, including antibodies or chemotherapeutic agents. Conclusion These preclinical results establish maplirpacept as an effective CD47 blocker that mitigates the potential for anemia in patients.
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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 | 0.001 |
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".