Polyphenolic natural products and natural product–inspired steroidal mimics as aromatase inhibitors
Bibliographic record
Abstract
The discovery of biologically active polyphenolic natural products, including chalcones, stilbenes, flavanones, and isoflavones as steroidal mimics has proven to be a subject of considerable importance in medicine. Some of these natural compounds have been shown to modulate key human metabolic processes via steroidal hormone receptors, or to inhibit crucial enzymes involved in the biosynthesis of steroidal hormones themselves. Isoflavone polyphenolics such as genistein are well known for this "phytoestrogenic" biological activity. This review focuses on the ability of select polyphenolics and their synthetic derivatives to function as steroidal mimics in the inhibition of the enzyme aromatase, thereby lowering production of endogenous estrogen growth hormones. The discovery of potent, natural product-based aromatase inhibitors (AIs) as hit compounds has led to the introduction of steroidal-based irreversible inhibitors, such as exemestane and reversible AIs such as anastrozole and letrozole, now standard therapy in the treatment of estrogen receptor-positive breast cancer and other hormone related indications. Pursuit of this strategy over the last few decades has been largely successful although complications and challenges remain. This review highlights the aromatase activity of natural stilbenes, chalcones, and flavanones and synthetically inspired versions thereof and draws attention to new and under-investigated areas within each class worthy of pursuit.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".