Zelenirstat Inhibits N-Myristoyltransferases to Disrupt Src Family Kinase Signaling and Oxidative Phosphorylation, Killing Acute Myeloid Leukemia Cells
Bibliographic record
Abstract
Acute myeloid leukemia (AML) is a hematologic malignancy with limited treatment options and a high likelihood of recurrence after chemotherapy. We studied N-myristoylation, the myristate modification of proteins linked to survival signaling and metabolism, as a potential therapeutic target for AML. N-myristoylation is catalyzed by two N-myristoyltransferases (NMT), NMT1 and NMT2, with varying expressions in AML cell lines and patient samples. We identified NMT2 expression as a marker for survival of patients with AML, and low NMT2 expression was associated with poor outcomes. We used the first-in-class pan-NMT inhibitor, zelenirstat, to investigate the role of N-myristoylation in AML. Zelenirstat effectively inhibits myristoylation in AML cell lines and patient samples, leading to degradation of Src family kinases, induction of endoplasmic reticulum stress, apoptosis, and cell death. Zelenirstat was well tolerated in vivo and reduced the leukemic burden in an ectopic AML cell line and in multiple orthotopic AML patient-derived xenograft models. The leukemia stem cell-enriched fractions of the hierarchical OCI-AML22 model were highly sensitive to myristoylation inhibition. Zelenirstat also impairs mitochondrial complex I and oxidative phosphorylation, which are critical for leukemia stem cell survival. These findings suggest that targeting N-myristoylation with zelenirstat represents a novel therapeutic approach for AML, with promise in patients with currently poor outcomes.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".