A Small Molecule Drug Screening Identifies the Antibiotic Colistin Sulfate as an Enhancer of NK Cell Cytotoxicity
Bibliographic record
Abstract
Cancer immunotherapy is an encompassing term referring to therapeutic strategies that aim to boost the immune system to fight cancer. These strategies include administering immune cells that have been altered to have greater anti-tumor activity or using biologics and small molecules that target immune components to also promote tumor clearance. Natural Killer (NK) cells are cells of the innate immune system that recognize and kill abnormal cells such as cancer cells and play an important role in the anti-tumor response. Because of their crucial role in tumor immunity, NK cells are prime targets for immunotherapies. Repurposing small molecule drugs is an attractive strategy to identify new immunotherapies from already approved drugs. Here, we screened 1,200 approved drugs from the Prestwick Chemical Library to identify drugs that increase NK cell cytotoxicity. We used a high-throughput luciferase-release cytotoxicity assay to measure the killing of the myeloid leukemia cell line, K562 cells expressing nano luciferase (NL) by NK92 cells, a human NK cell line. From the drug candidates identified from the screening assay, the antibiotic colistin sulfate increased cytotoxicity of the NK92 cell line and unstimulated human NK cells towards K562-NL cells. This increase in NK cytotoxicity was short-lived as pre-treating NK92 cells with colistin for 1 hour or 24 hours did not increase cytotoxicity. Also, we show pre-treating K562-NL target cells with colistin does not sensitize them to NK-mediated killing. Further studies are needed to uncover the mechanism of action of colistin, thus contributing to knowledge of fundamental NK cell biology regarding NK cell cytotoxicity which will aid in identifying additional small molecule drugs that enhance NK cell activity.
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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 | 0.001 | 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".