Abstract PD6-7: In-silico discovery of novel estrogen receptor-α inhibitors as potential therapeutics for tamoxifen resistant breast cancer
Bibliographic record
Abstract
Abstract Estrogen receptor-α (ER) positive breast cancer (BCa) represents 75% of all invasive BCas. Conventional ER-directed drug Tamoxifen targets the estrogen binding pocket (EBP) of the receptor. However, over prolonged periods of treatment, the therapeutic efficacy of Tamoxifen declines due to development of resistance. Numerous factors are causative for this phenomenon, including recently reported mutations in the receptor (T537S). Therefore, there is an urgency to develop novel anti-ER therapeutics that exhibit entirely different mode of ER inhibition. A promising alternative strategy is to prevent receptor-coactivator interaction and block further crucial steps in ER activity. ER-coactivator interface should be less prone to adaptive mutations as any mutations at this site would also likely block the coactivator recruitment, we therefore targeted the Activation Function-2 (AF2) site, a coactivator binding pocket on ER, called to overcome the limitations of Tamoxifen. Although AF2 is a shallow surface pocket, the pharmacophore-rich features of this site make it a druggable target. To identify potential ER AF2 inhibitors, virtual screening was performed. Initial hits were subjected to lead optimization and more potent analogues were rationally designed by exploiting critical features of this site. Potential compounds were tested for their ability to inhibit ER transcriptional activity using the T47D-KBluc cell line stably transfected with an ER-specific luciferase reporter. Consequently, the lead compound VPC-16339 (IC50=8.24µM) was identified. The direct binding between VPC-16339 and the receptor was confirmed by Biolayer Interferometry assay. More importantly, VPC-16339 prevents coactivator recruitment at the AF2 pocket in a dose dependent manner as measured by TR-FRET coactivator recruitment assay. Increasing concentrations of estradiol did not affect the IC50 of the lead compound, thereby ruling out the possibility of VPC-16339 binding to EBP. VPC-16339 demonstrated a strong anti-proliferative effect on MCF7 and Tamoxifen resistant cells, with no effect on ER- HeLa cells, suggesting its selective ER-mediated action, as further validated by ER luciferase assay in Tamoxifen resistant cells. VPC-16339 effectively inhibits mRNA and protein expression levels of the estrogen dependent genes such as pS2,CathD and CDC2. Due to AF2-guided mechanism, VPC-16339 successfully overcomes Tamoxifen resistance and inhibits the constitutively active Tamoxifen resistant form of ER (T537S). In summary, we report VPC-16339 as an ER AF2 specific inhibitor with promising anti-proliferative effect in BCa cell lines including Tamoxifen resistant cell lines. VPC-16339 effectively inhibits the mutant form of the receptor (T537S) which is responsible for acquired endocrine resistance. It can be anticipated that ER AF2 inhibitors will provide an alternative therapeutic strategy that can be applied concurrently or simultaneously with current anti-ER treatments for BCa patients with advanced disease. Citation Format: Kriti Singh, Ravi SN Munuganti, Miriam Butler, Artem Cherkasov, Paul S Rennie. In-silico discovery of novel estrogen receptor-α inhibitors as potential therapeutics for tamoxifen resistant breast cancer [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr PD6-7.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".