Precision-Oriented Theophylline-Platinum(IV) Prodrugs: Eliciting Synthetic Lethality in BRCA1-Deficient Ovarian Cancer with Enhanced Efficacy and Reduced Toxicity <i>In Vitro</i> and <i>In Vivo</i>
Bibliographic record
Abstract
Ovarian cancer (OC) is a lethal gynecologic malignancy with limited treatments. Platinum(II) drugs are commonly used but faced severe toxicities and resistance. This study developed theophylline-platinum(IV) prodrugs ( 1 – 8 ) to combat BRCA1-deficient OC via synthetic lethality strategy. Representative compound 4 displayed the most potent antitumor effect by synergizing theophylline-induced PARP-1 inhibition with platinum-induced DNA damage to fully exert synthetic lethality in BRCA1-deficient cells with homologous recombination repair deficiencies. In vitro, 4 exhibited 80- and 581-fold higher antiproliferative activities than cisplatin in SKOV3 and SKOV3-BRCA1-KD cells, respectively. Subsequent tests revealed 4 enhanced DNA damage, ROS production, mitochondrial dysfunction, and S-phase arrest, along with reducing invasion and metastasis. In SKOV3-BRCA1-KD xenograft models, 4 exhibited 71.70% tumor growth inhibition, surpassing cisplatin (50.48%) and olaparib (47.63%), with mitigated nephrotoxicity. Immunohistochemistry showed PARP-1 suppression (74.68% to 9.14%), validating synthetic lethality mechanism. These findings underscore theophylline-Pt(IV) prodrugs potential in overcoming platinum(II) drugs limitations and advancing personalized oncology.
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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".