Abstract A2: Development of a phenotypic profiling platform with high predictive value for the identification of novel antiangiogenic drugs.
Notice bibliographique
Résumé
Abstract Deregulation of angiogenesis plays a major role in a number of human diseases, most notably cancer. Although angiogenesis inhibitors are among the most promising anticancer drug candidates, existing FDA approved drugs have shown limited efficacy in the clinic. The majority of angiogenesis inhibitors under clinical development have been designed with the help of high-throughput screening techniques focused on single molecular targets. Although these methods have yielded several candidates, it has been long recognized that a lack of correlation with activity in in vivo preclinical models has resulted in high levels of attrition during the early stages of drug discovery. Here we introduce a novel high-content cell-specific fluorescence platform for discovery of antiangiogenic agents, which we have validated by screening the 1970 small molecules part of the NCI Diversity Set. The platform features a primary screening based on high content growth and tube formation assays using phenotypically defined fluorescent reporter endothelial cells. Tube formation assays were performed using VEGFR2-nonexpressing endothelial cells and quantitatively evaluated in an automated fashion with the newly developed image analysis software AngioApplication. 2.3% (46) of all the small molecules in the library showed growth inhibition activity and 3.5% (70) significantly blocked tube formation. Interestingly, 0.5% (11) of the small molecules showed growth and tube formation inhibitory activity. None of the lead compounds interfered with tubulin polymerization or inhibited receptor tyrosine kinase activity. Seven lead compounds were evaluated in xenograft tumor angiogenesis models. All showed anti-tumor activity, and two of the compounds (CID 5458317 and CID 429599) blocked tumor growth in an in vivo leiomyosarcoma xenograft model of angiogenesis with comparable efficacy as bevacizumab. Gene expression profiling showed that the number of proangiogenic pathways down-regulated in endothelial cells recovered from drug-exposed tube formation assays was predictive of tumor growth inhibition in vivo. High-throughput chicken chorioallantoic assays closely mimicked the efficacy of tested drugs in the tumor xenograft models and supported an antiangiogenic mechanism of action. Histological assessment of xenograft tumors treated with CID 5458317 showed a drastic diminution of their vascular network compared to vehicle treated tumors. Preliminary data using intravital microscopy on a mouse dorsal skin chamber model showed massive leakiness and vascular regression in tumor vasculature exposed to CID 5458317. In conclusion, we have developed a platform for the identification of novel antiangiogenic drugs with high predictive value. Using this platform, several small molecules with potentially novel antiangiogenic mechanisms of action have been identified. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr A2.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».