Parthenolide's anti‐leukemic stem cell activity is enhanced by the inhibition of dipeptidyl peptidase 8 and 9
Bibliographic record
Abstract
Parthenolide (PTL) is the bioactive component of the medicinal plant, Feverfew, and is sold as an herbal extract for the treatment of migraines. It induces specific toxicity to leukemia stem cells; however, PTL also activates cell protective effects that limit its clinical application. Therefore, we sought to identify agents that synergistically enhance PTL's stem cell cytotoxicity. Using a high‐throughput combination drug screen, we identified the oral hypoglycemic, vildagliptin, which synergized with PTL to induce death of the leukemia stem cell line, TEX (combination index (CI) = 0.36 and 0.16, at EC 50 and 80, respectively; where CI < 1 denotes statistical synergy). The combination of PTL and vildagliptin reduced the viability of cells from acute myeloid leukemia patients but had no effect on the viability of normal human peripheral blood stem cells. The basis for synergy was independent of vildagliptin's primary action as an inhibitor of dipeptidyl peptidase (DPP) 4. Rather, using chemical and genetic approaches we demonstrated that the synergy was due to inhibition of the related enzymes DPP 8 and 9. In summary, these results highlight DPP 8 and 9 inhibition as a novel chemosensitizing strategy in leukemia stem cells. Moreover, these results suggest that the combination of vildagliptin and PTL could be useful for the treatment of leukemia.
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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".