Hepoxilin analogs, PBT-3 and PBT-4, cause apoptosis of Gleevec-resistant K562 cells in vitro.
Bibliographic record
Abstract
The use of Gleevec in the treatment of leukemia has been widely accepted, although resistance to Gleevec is commonly observed. Gleevec represents a new direction in the development of target-focused chemotherapeutic agents in cancer. Gleevec inhibits the tyrosine kinase activity of Bcr-Abl, which is responsible for leukemic cell survival. We have previously shown that PBT-3 (racemic anti-10(R/S)-hydroxy-11, 12-cyclopropyl-eicosa-5Z, 8Z, 14Z-trienoic acid methyl ester) and PBT-4 (racemic syn- 10(R/S)-hydroxy- 11,12-cyclopropyleicosa-5Z 8Z, 14Z-trienoic acid methyl ester), stable analogs of the hepoxilins, caused apoptosis of the human leukemic K562 cell line in vitro and in vivo. We also showed that PBTs inhibited the growth of tumours derived from the inoculation of immunodeficient mice with K562 cells and that the effect of PBTs was synergistic with that of Gleevec. We now show that the effect of PBT-3 and of PBT-4 is independent of that of Gleevec, demonstrating that Gleevec-resistant K562 cells retain their responsiveness to PBT treatment, resulting in apoptosis. These findings provide important information suggesting that the two compounds, PBT and Gleevec, can be used together in the treatment of leukemia. The PBTs may provide a new platform for the development of apoptotic drugs in 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 | 0.001 |
| 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".