Abstract IA008: Leveraging conformational dynamics for improved selectivity
Notice bibliographique
Résumé
Abstract Maximizing the clinical activity and combinability of oncogene inhibitors requires the development of molecules that can achieve necessary levels of target inhibition at a tolerated and feasible human dose. This is enabled by improving selectivity against off-targets and/or for the mutant form of the target over wild-type. While conventional structural insights can inform the design of selective drug candidates, there are often cases where clear selectivity hypotheses are not readily identifiable. Here, we present two examples illustrating how conformational dynamics can be leveraged to obtain improved selectivity for validated oncogene targets. The first example focuses on the selective inhibition of FGFR2, a well-established target in various cancers harboring FGFR2 fusions or rearrangements. Although clinical efficacy of pan-FGFR inhibitors has been demonstrated, their benefit is limited by FGFR1- and FGFR4- mediated toxicities. We leveraged differences in conformational dynamics between FGFR2 and other FGFRs observed through molecular dynamics simulations to enable the development of Lirafugratinib, the first FGFR2 selective inhibitor. Lirafugratinib inhibits FGFR2 with a high degree of selectivity in pre-clinical models. In cancer patients, Lirafugratinib achieves >95% FGFR2 occupancy at a dose of 70mg once daily, with minimal evidence of inhibition of other FGFR isoforms. This improved selectivity translates to higher objective response rates compared to pan-FGFR inhibitors across multiple tumor types harboring FGFR2 fusions or rearrangements. The second example illustrates mutant-selective targeting of PI3Kα, the most frequently mutated kinase in cancer. While non-mutant selective inhibitors have shown clinical efficacy, their benefit is limited by hyperglycemia caused by inhibition of wild-type PI3Kα. Conformational differences between mutant and wild-type PI3Kα were identified using structural insights combined with molecular dynamics simulations, leading to the discovery of RLY-2608- the first mutant-selective inhibitor of PI3Kα. RLY-2608 binds to a novel allosteric pocket and demonstrates mutant selective inhibition in pre-clinical models. RLY-2608 has a favorable pharmacokinetic profile in cancer patients, with dose-dependent increases in exposure and low peak to trough fluctuations, resulting in high levels of target coverage throughout the dosing interval. When combined with fulvestrant, this translates into a higher objective response rate and lower rate of hyperglycemia in patients with hormone-receptor positive, PI3Kα-mutant breast cancer compared to non-mutant selective PI3Kα inhibitors. These examples demonstrate the power of leveraging conformational dynamics to obtain selective target inhibition, ultimately translating into improved patient outcomes. Citation Format: James Watters. Leveraging conformational dynamics for improved selectivity [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr IA008.
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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 ».