Macrocyclic lactones inhibit nasopharyngeal carcinoma cells proliferation through PAK1 inhibition and reduce in vivo tumor growth
Bibliographic record
Abstract
PURPOSE: The Epstein-Barr virus (EBV)-associated cancer nasopharyngeal carcinoma (NPC) is rare in Europe and North America but is a real public health problem in some regions of the world, such as southern Asia, North Africa, and for Inuit populations. Due to the anatomy and location of the nasopharynx, surgery is rarely used to treat primary NPC cancers. Treatment by radiotherapy, combined or not with chemotherapy, are efficient for primary tumors but often do not protect against fatal relapses or metastases. METHODS: Search for new therapeutic molecules through high content screening lead to the identification of Ivermectin (IVM) as a promising drug. IVM is a US Food and Drug Administration-approved macrocyclic lactone widely used as anthelmintic and insecticidal agent that has also shown protective effects against cancers. RESULTS: We show here that IVM has cytotoxic activity in vitro against NPC cells, in which it reduces MAPKs pathway activation through the inhibition PAK-1 activity. Moreover, all macrocyclic lactones tested and a PAK1 inhibitor are cytotoxic in vitro for EBV-positive and EBV-negative NPC tumor cells. We have also shown that IVM intraperitoneal repeated injections, at US Food and Drug Administration-approved doses, have no significant toxicity and decrease NPC subcutaneous tumors development in nude mice. CONCLUSION: Macrocyclic lactones appear as promising molecules against NPC targeting PAK-1 with no detectable adverse effect.
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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) | 0.002 | 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".