Potential Use of the Anti-Inflammatory Drug, Sulfasalazine, for Targeted Therapy of Pancreatic Cancer
Bibliographic record
Abstract
Pancreatic cancer is an aggressive, drug-resistant disease; its first-line chemotherapeutic, gemcitabine, is only marginally effective. Intracellular depletion of glutathione, a major free-radical scavenger, has been associated with growth arrest and reduced drug resistance (chemosensitization) of cancer cells. In search of a new therapeutic approach for pancreatic cancer, we sought to determine whether specific inhibition of the plasma membrane x(c) (-) cystine transporter could lead to reduced uptake of cysteine, a key precursor of glutathione, and subsequent glutathione depletion. Sulfasalazine (approximately 0.2 mmol/L), an anti-inflammatory drug with potent x(c) (-)-inhibitory properties, markedly reduced l¹⁴C]-cystine uptake, glutathione levels, and growth and viability of human MIA PaCa-2 and PANC-1 pancreatic cancer cells in vitro. These effects were shown to result primarily from inhibition of cystine uptake mediated by the x(c) (-) cystine transporter and not from inhibition of nuclear factor kappaB activation, another property of sulfasalazine. The efficacy of gemcitabine could be markedly enhanced by combination therapy with sulfasalazine both in vitro and in immunodeficient mice carrying xenografts of the same cell lines. No major side effects were observed in vivo.The results of the present study suggest that the x(c) (-) transporter plays a major role in pancreatic cancer by sustaining or enhancing glutathione biosynthesis, and as such, represents a potential therapeutic target. Sulfasalazine, a relatively nontoxic drug approved by the U.S. Food and Drug Administration, may, in combination with gemcitabine, lead to more effective therapy of refractory pancreatic cancer.
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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".