Role of Natural and Synthetic Flavonoids as Potential Aromatase Inhibitors in BreastCancer: Structure-Activity Relationship Perspective
Bibliographic record
Abstract
World Health Organization categorized breast cancer as one of the leading cancer types in females worldwide, and its treatment remains challenging. Accumulated evidence suggested the role of estrogen and its metabolites in pre- and post-menopausal women. Upregulation of estrogen-dependent aromatase is significantly involved in the pathogenesis of breast cancer. Several aromatase inhibitors, such as exemestane, formestane, and letrozole, are being used clinically, owing to their estrogen suppression role. Apart from these drugs, several other molecules, such as natural and synthetic flavonoids, have been reported widely for a similar biological activity. However, some reasonable modifications are required for these structures to achieve desired efficacy and to alleviate toxicity. Designing a novel aromatase inhibitor will be possible if we can establish a rational correlation between the chemistry and biological features of the existing molecules. The benzopyranone- ring system, present in the flavonoid molecules, has been reported as a pharmacophore due to its inhibitory activity on aromatase, which helps repress breast cancer progression. This essential feature has been utilized to modify several natural flavonoids into 5 and 7 hydroxy/methoxy flavone, 4-imidazolyl/triazolyl flavone, 5,4'- diamino flavone, 7,8- benzo-4-imidazolyl flavone, α-naphthoflavone, and 2-azole/thiazolyl isoflavone derivatives. These scaffolds have been considered in this review for meticulous study in aspects of the structure-activity relationship for aromatase inhibitory activity, and it would likely pave the way for designing a potential lead candidate in the future.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".