Abstract 2521: Small molecule inhibitors targeting the activation function-2 site of estrogen receptor-α
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».