Abstract 2521: Small molecule inhibitors targeting the activation function-2 site of estrogen receptor-α
Bibliographic record
Abstract
Abstract Approximately 75% of Breast Cancers (BCas) are classified as Estrogen Receptor alpha (ERα) positive. Treatment with anti-estrogens such as Tamoxifen has been the main therapeutic approach for more than 30 years. However, one third of women treated with Tamoxifen for 5 years develop recurrent disease. Experimental and clinical observations have suggested that ERα signalling continues to play an important role even after the development of resistance. Moreover, biopsies from BCa patients who relapsed on Tamoxifen indicated that ERα expression was retained in more than 50% of the cases. Because of the emergence of hormone resistance, there is a clear need to develop entirely novel anti-ERα therapeutics, such as drugs that would directly disrupt the interaction between ERα and its coactivator proteins at the corresponding regulatory interfaces, exemplified by a well-characterized Activation Function-2 (AF-2) site.In the current study we have used state of artificial intelligence systems to rationally select new anti-ERα drug candidates. Using the power of modern computers, we performed large-scale docking using millions of existing chemicals from the ZINC database and identified several promising small molecules as candidate AF-2 binders. We then conducted biological screens to identify compounds that can bind to the AF-2 pocket and inhibit ERα transactivation. A reporter assay was developed using T47D-Kbluc breast cancer cells, a line which had been stably transfected with an estrogen responsive luciferase reporter gene construct consisting of three estrogen response elements (EREs) upstream of a TATA promoter, to evaluate the potential of these compounds to inhibit ERα transcriptional activity. Compounds that inhibited ERα-mediated transcription of the reporter gene in a concentration dependent manner were further analysed. These compounds do not displace estrogen, but block ERα-coactivator interaction, as measured by TR-FRET assay, thereby confirming that inhibition of coactivator recruitment is not by the allosteric mechanism of conventional antagonists. One of our best compounds, VPC-16046, shows direct reversible binding to the ERα ligand binding domain as detected by Biolayer Interferometry assay. This compound demonstrated a strong anti-proliferative effect on MCF7, T47D and Tamoxifen resistant cells without affecting the growth of ERα-negative HeLa cells, used as a control in MTS assay.In summary, our study has identified a novel class of ERα AF2 inhibitors that have the potential to effectively inhibit ERα transcriptional activity by a mechanism which does not target the estrogen binding site and thereby circumvents treatment resistance seen with conventional, clinically used anti-estrogens. Treatment with these inhibitors should lead to a substantial improvement in the survival rate of women with advanced Tamoxifen-resistant BCa. Citation Format: Kriti Singh, Ravi Shashi Nayana Munuganti, Eric Leblanc, Artem Cherkasov, Paul S. Rennie. Small molecule inhibitors targeting the activation function-2 site of estrogen receptor-α. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2521. doi:10.1158/1538-7445.AM2014-2521
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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 | 0.000 | 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 | 0.001 |
| 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".