Potential Use of the Anti-Inflammatory Drug, Sulfasalazine, for Targeted Therapy of Pancreatic Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it