Anticancer activity of pyrithione zinc in oral cancer cells identified in small molecule screens and xenograft model: Implications for oral cancer therapy
Bibliographic record
Abstract
Oral squamous cell carcinoma (OSCC) patients diagnosed in late stages have limited chemotherapeutic options, underscoring the great need for development of new anticancer agents for more effective disease management. We aimed to identify novel anticancer agents for OSCC using quantitative high throughput assays for screening six chemical libraries consisting of 5170 small molecule inhibitors. In depth characterization resulted in identification of pyrithione zinc (PYZ) as the most effective cytotoxic agent inhibiting cell proliferation and inducing apoptosis in OSCC cells in vitro. Further, treatment with PYZ reduced colony forming, migration and invasion potential of oral cancer cells in a dose-dependent manner. PYZ treatment also led to altered expression of several key components of the major signaling pathways including PI3K/AKT/mTOR and WNT/β-catenin in OSCC cells. In addition, treatment with PYZ also reduced expression of 14-3-3ζ, 14-3-3σ, cyclin D1, c-Myc and pyruvate kinase M2 (PKM2), proteins identified in our earlier studies to be involved in development and progression of OSCCs. Importantly, PYZ treatment significantly reduced tumor xenograft volume in immunocompromised NOD/SCID/Crl mice without causing apparent toxicity to normal tissues. Taken together, we demonstrate in vitro and in vivo efficacy of PYZ in OSCC. In conclusion, we identified PYZ in HTS assays and demonstrated in vitro and in vivo pre-clinical efficacy of PYZ as a novel anticancer therapeutic candidate in OSCC.
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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".