Abstract PD6-7: In-silico discovery of novel estrogen receptor-α inhibitors as potential therapeutics for tamoxifen resistant breast cancer
Notice bibliographique
Résumé
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.
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) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».