Abstract P246: Discovery and characterization of selective, FGFR1 sparing, inhibitors of FGFR2/3 oncogenic mutations for the treatment of cancers
Notice bibliographique
Résumé
Abstract Background: Targeting FGFR genetic alterations using small molecule inhibitors is a validated therapeutic strategy for urothelial carcinoma and cholangiocarcinoma. However, the current FDA-approved pan-FGFR inhibitors, erdafitinib and pemigatinib, are subject to FGFR1-mediated dose-limiting toxicities (e.g., hyperphosphatemia). These treatments necessitate a high rate of dose reductions, interruptions, and discontinuations, thereby potentially limiting efficacy. In addition, drug-resistant mutations (e.g., gatekeeper) in FGFR2 and FGFR3 genes rapidly emerge in patients treated with these drugs. Our research goals are to reveal the full spectrum of oncogenic FGFR2 and FGFR3 mutations that drive tumor growth and to discover an inhibitor that selectively targets these mutations together with FGFR2 and FGFR3 gene fusion and drug-resistance mutations, while minimizing FGFR1 activity and associated toxicities. We hypothesize that this will deliver an FGFR precision medicine with enhanced anti-tumor activity, an improved drug resistance profile, and broader mutational coverage. Methods: Applying the Mutation-Allostery-Pharmacology (MAP) platform technology developed by Black Diamond Therapeutics, we defined a spectrum of 34 allosteric FGFR2/3 oncogenic mutations, including over 28 previously uncharacterized mutations that we now show to be oncogenic. The MAP platform allowed us to further classify those mutations into functional clusters or families of mutations that can be targeted using a single compound. While located throughout the extracellular and kinase domains, we demonstrated how these functional clusters activate FGFR2 or FGFR3. Among the mechanisms identified, a functional cluster of mutations is activated due to disulfide-bond mediated dimerization. Results: Herein, we report the discovery of a series of orally available, selective FGFR2/3 inhibitors that 1) shows antiproliferative potency across all 34 mutations; 2) spares FGFR1-wild-type; 3) is active against gatekeeper mutations and 4) shows favorable selectivity versus a subset of closely related kinases in the human kinome. In addition to being potent against FGFR2 and FGFR3 primary mutations, we demonstrated that our FGFR1 sparing inhibitors retain potency against the most prevalent FGFR2 resistant mutations. When dosed orally, one example was well tolerated and exhibited dose-dependent PK/PD and anti-tumor efficacy and regression in several FGFR2 and FGFR3 driven xenograft models in mice. In addition, when dosed at efficacious doses, no FGFR1-mediated hyperphosphatemia was observed in these animals instead of the animals that were treated with pan FGFR inhibitors. Conclusion: Our data support the development of rationally designed selective inhibitors targeting a spectrum of FGFR2/3 mutations while sparing dose limiting FGFR1 activity. Citation Format: Etienne Dardenne, Fernando Padilla, Sara Rasmussen, Shao Ning Yang, Ahmet Mentes, Luisa Shin Ogawa, Anthony Trombino, Darlene Romashko, Maria Chevtsova, Shalabh Thakur, Elisabeth Buck, Christopher Roberts, Matthew Lucas, Tai-An Lin. Discovery and characterization of selective, FGFR1 sparing, inhibitors of FGFR2/3 oncogenic mutations for the treatment of cancers [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P246.
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 ».