Targeting p53 by small molecules in hematological malignancies
Bibliographic record
Abstract
p53 is a powerful tumor suppressor and is an attractive cancer therapeutic target. A breakthrough in cancer research came from the discovery of the drugs which are capable of reactivating p53 function. Most anti-cancer agents, from traditional chemo- and radiation therapies to more recently developed non-peptide small molecules exert their effects by enhancing the anti-proliferative activities of p53. Small molecules such as nutlin, RITA, and PRIMA-1 that can activate p53 have shown their anti-tumor effects in different types of hematological malignancies. Importantly, nutlin and PRIMA-1 have successfully reached the stage of phase I/II clinical trials in at least one type of hematological cancer. Thus, the pharmacological activation of p53 by these small molecules has a major clinical impact on prognostic use and targeted drug design. In the current review, we present the recent achievements in p53 research using small molecules in hematological malignancies. Anticancer activity of different classes of compounds targeting the p53 signaling pathway and their mechanism of action are discussed. In addition, we discuss how p53 tumor suppressor protein holds promise as a drug target for recent and future novel therapies in these diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".