Abstract A013: Prioritization of Eleven-Nineteen-Leukemia (ENL) inhibitors as orally available drug candidates for acute myeloid leukemia
Bibliographic record
Abstract
Abstract Acute myeloid leukemia (AML) is the second most prevalent and fatal form of leukemia. The growth of AML cells harboring oncogenic MLL rearrangements relies on The YEATS domain-containing protein ENL. Many small molecule inhibitors targeting ENL have been developed. To prioritize these inhibitors for in vivo studies, a NanoBRET system was introduced to evaluate their cellular permeability and potency. This screening identified Inhibitor 13 as a promising candidate. This inhibitor has remarkable metabolic stability and potent anti-proliferative effects on MLL-fusion leukemia cell lines. In AML-xenografted mice, Inhibitor 13 significantly improved survival. Subsequent optimization efforts led to the development of SR-C-107 (R), which exhibited strong activity against AML both at the cellular level (CC50 (MOLM-13): 1.25 ± 0.18 μM; CC50 (MV4-11): 0.81 ± 0.15 μM) and in vivo. These findings establish SR-C-107 (R) as a compelling candidate for AML treatment and lay the groundwork for the development of next-generation AML inhibitors. Citation Format: Wenshe R. Liu, Xuejiao Guo, Sandeep Alta, Shiqing Xu. Prioritization of Eleven-Nineteen-Leukemia (ENL) inhibitors as orally available drug candidates for acute myeloid leukemia. [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 A013
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".