Unlocking the potential of aromatase inhibitors: recent advances in drug design, synthesis, docking activity, and in vitro bioactivity evaluations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Breast cancer, a global health concern claiming approximately 685,000 lives in 2020, necessitates continual advancements in therapeutic strategies. Estrogen and aromatase play pivotal roles in hormone-responsive breast cancer, with 80% of patients exhibiting estrogen receptor-positive tumors. Aromatase inhibitors (AIs), notably non-steroidal inhibitors like anastrozole and letrozole, have significantly improved outcomes, yet challenges persist, including side effects. This review focuses on recent developments in AIs, exploring xanthone derivatives, imidazole derivatives, and curcumin derivatives as potential inhibitors of aromatase. Molecular docking studies, employing Auto Dock and other tools, reveal the binding affinities and interactions of these compounds with the aromatase enzyme. Among xanthones, Erythrommone emerges as a potent inhibitor, holding promise for clinical trials. Imidazole derivatives, synthesized through the Debus-Radziszewski reaction, demonstrate anticancer potential, with compounds like 1a exhibiting superior efficacy against MCF7 cells. ADME-Tox analyses indicate promising drug-likeness but reveal potential mutagenic effects and environmental impacts. Curcumin derivatives, particularly 1,5-diaryl-1,4-pentadien-3-ones, present alternatives to address curcumin's bioavailability challenges. A study of 25 compounds (DKC) identifies DKC-10 as a potent inhibitor, outperforming established breast cancer drugs in terms of binding affinity and interactions with aromatase and ERα+ receptors. These findings underscore the importance of exploring diverse chemical structures in developing AIs, paving the way for more effective and well-tolerated therapeutics. The integration of computational techniques, such as molecular docking studies, accelerates drug discovery by predicting interactions at the molecular level. Overall, this comprehensive review provides valuable insights into the evolving landscape of aromatase inhibitors, offering a roadmap for future research and the development of advanced breast cancer therapeutics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it