Abstract P246: Discovery and characterization of selective, FGFR1 sparing, inhibitors of FGFR2/3 oncogenic mutations for the treatment of cancers
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".