Efficacy and Synergy of Small Molecule Inhibitors Targeting FLT3-ITD+ Acute Myeloid Leukemia
Bibliographic record
Abstract
Constitutive activation of FLT3 by ITD mutations is one of the most common genetic aberrations in AML, present in ~1/3 of cases. Patients harboring FLT3-ITD display worse clinical outcomes. The integration and advancement of FLT3 TKI in AML treatment provided significant therapeutic improvement. However, due to the emergence of resistance mechanisms, FLT3-ITD+ AML remains a clinical challenge. We performed an unbiased drug screen to identify 18 compounds as particularly efficacious against FLT3-ITD+ AML. Among these, we characterized two investigational compounds, WS6 and ispinesib, and two approved drugs, ponatinib and cabozantinib, in depth. We found that WS6, although not yet investigated in oncology, shows a similar mechanism and potency as ponatinib and cabozantinib. Interestingly, ispinesib and cabozantinib prevent activation of AXL, a key driver and mechanism of drug resistance in FLT3-ITD+ AML patients. We further investigated synergies between the selected compounds and found that combination treatment with ispinesib and cabozantinib or ponatinib shows high synergy in FLT3-ITD+ AML cell lines and patient samples. Together, we suggest WS6, ispinesib, ponatinib and cabozantinib as novel options for targeting FLT3-ITD+ AML. Whether combinatorial tyrosine kinase and kinesin spindle blockade is effective in eradicating neoplastic (stem) cells in FLT3-ITD+ AML remains to be determined in clinical trials.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".