Selective ERK1/2 agonists isolated from Melia azedarach with potent anti-leukemic activity
Bibliographic record
Abstract
BACKGROUND: MAPK/ERK kinases transmit signals from many growth factors/kinase receptors during normal cell growth/differentiation, and their dysregulation is a hallmark of diverse types of cancers. A plethora of drugs were developed to block this kinase pathway for clinical application. With the exception of a recently identified agent, EQW, most of these inhibitors target upstream factors but not ERK1/2; no activator of ERK1/2 is currently available. METHOD: A library of compounds isolated from medicinal plants of China was screened for anti-cancer activities. Three limonoid compounds, termed A1541-43, originally isolated from the plant Melia azedarach, exhibiting strong anti-leukemic activity. The anti-neoplastic activity and the biological target of these compounds were explored using various methods, including western blotting, flow cytometry, molecular docking and animal model for leukemia. RESULTS: Compounds A1541-43, exhibiting potent anti-leukemic activity, was shown to induce ERK1/2 phosphorylation. In contrast, the natural product Cedrelone, which shares structural similarities with A1541-43, functions as a potent inhibitor of ERK1/2. We provided evidence that A1541-43 and Cedrelone specifically target ERK1/2, but not the upstream MAPK/ERK pathway. Computational docking analysis predicts that compounds A1541-43 bind a region in ERK1/2 that is distinct from that to which Cedrelone and EQW bind. Interestingly, both A1541-43, which act as ERK1/2 agonists, and Cedrelone, which inhibit these kinases, exerted strong anti-proliferative activity against multiple leukemic cell lines, and induced robust apoptosis as well as erythroid and megakaryocytic differentiation in erythroleukemic cell lines. These compounds also suppressed tumor progression in a mouse model of erythroleukemia. CONCLUSIONS: This study identifies for the first time activators of ERK1/2 with therapeutic potential for the treatment of cancers driven by dysregulation of the MAPK/ERK pathway and possibly for other disorders.
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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) | 0.002 | 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".