Repurposing pentamidine for cancer immunotherapy by targeting the PD1/PD-L1 immune checkpoint
Bibliographic record
Abstract
Immunotherapy has emerged as an effective therapeutic approach to several cancer types. The reinvigoration of tumor-infiltrating lymphocyte-mediated immune responses via the blockade of immune checkpoint markers, such as program cell death-1 (PD-1) or its cognate ligand PD-L1, has been the basis for developing clinically effective anticancer therapies. We identified pentamidine, an FDA-approved antimicrobial agent, as a small-molecule antagonist of PD-L1. Pentamidine enhanced T-cell-mediated cytotoxicity against various cancer cells in vitro by increasing the secretion of IFN-γ, TNF-α, perforin, and granzyme B in the culture medium. Pentamidine promoted T-cell activation by blocking the PD-1/PD-L1 interaction. In vivo administration of pentamidine attenuated the tumor growth and prolonged the survival of tumor-bearing mice in PD-L1 humanized murine tumor cell allograft models. Histological analysis of tumor tissues showed an increased number of tumor-infiltrating lymphocytes in tissues derived from pentamidine-treated mice. In summary, our study suggests that pentamidine holds the potential to be repurposed as a novel PD-L1 antagonist that may overcome the limitations of monoclonal antibody therapy and can emerge as a small molecule cancer immunotherapy.
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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 | 0.000 |
| 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".