Benzothiophenone Derivatives Targeting Mutant Forms of Estrogen Receptor-α in Hormone-Resistant Breast Cancers
Bibliographic record
Abstract
Estrogen receptor-α positive (ERα⁺) breast cancers represent 75% of all invasive breast cancer cases, while de novo or acquired resistance to ER-directed therapy is also on the rise. Numerous factors contribute to this phenomenon including the recently-reported ESR1 gene mutations such as Y537S, which amplifies co-activator interactions with ERα and promotes constitutive activation of ERα function. Herein, we propose that direct targeting of the activation function-2 (AF2) site on ERα represents a promising alternative therapeutic strategy to overcome mutation-driven resistance in breast cancer. A systematic computer-guided drug discovery approach was employed to develop a potent ERα inhibitor that was extensively evaluated by a series of experiments to confirm its AF2-specific activity. We demonstrate that the developed small-molecule inhibitor effectively prevents ERα-coactivator interactions and exhibits a strong anti-proliferative effect against tamoxifen-resistant cells, as well as downregulates ERα-dependent genes and effectively diminishes the receptor binding to chromatin. Notably, the identified lead compound successfully inhibits known constitutively-active, resistance-associated mutant forms of ERα observed in clinical settings. Overall, this study reports the development of a novel class of ERα AF2 inhibitors, which have the potential to effectively inhibit ERα activity by a unique mechanism and to circumvent the issue of mutation-driven resistance in breast cancer.
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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".