Therapeutic opportunities in cancer therapy: targeting the p53-MDM2/MDMX interactions.
Bibliographic record
Abstract
Ubiquitination is a key enzymatic post-translational modification that influences p53 stability and function. p53 protein regulates the expression of MDM2 (mouse double-minute 2 protein) E3 ligase and MDMX (double-minute 4 protein), through proteasome-based degradation. Exploration of targeting the ubiquitination pathway offers a potentially promising strategy for precision therapy in a variety of cancers. The p53-MDM2-MDMX pathway provides multiple molecular targets for small molecule screening as potential therapies for wild-type p53. As a result of its effect on molecular carcinogenesis, a personalized therapeutic approach based on the wild-type and mutant p53 protein is desirable. We highlighted the implications of p53 mutations in cancer, p53 ubiquitination mechanistic details, targeting p53-MDM2/MDMX interactions, significant discoveries related to MDM2 inhibitor drug development, MDM2 and MDMX dual target inhibitors, and clinical trials with p53-MDM2/MDMX-targeted drugs. We also investigated potential therapeutic repurposing of selective estrogen receptor modulators (SERMs) in targeting p53-MDM2/MDMX interactions. Molecular docking studies of SERMs were performed utilizing the solved structures of the p53/MDM2/MDMX proteins. These studies identified ormeloxifene as a potential dual inhibitor of p53/MDM2/MDMX interaction, suggesting that repurposing SERMs for dual targeting of p53/MDM2 and p53/MDMX interactions is an attractive strategy for targeting wild-type p53 tumors and warrants further preclinical research.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".