A safety evaluation of omacetaxine mepesuccinate for the treatment of chronic myeloid leukemia
Bibliographic record
Abstract
INTRODUCTION: Therapy of chronic myeloid leukemia (CML) has been completely transformed by the development of tyrosine kinase inhibitors (TKIs). However, a subset of patients will fail TKI therapy due to resistance or intolerance. Omacetaxine mepesuccinate (OM), a protein translation inhibitor, is currently the only approved therapy that does not directly target the kinase domain. It has activity for CML patients irrespective of the phase or underlying kinase domain mutation status. AREAS COVERED: We searched the MEDLINE database for articles published in English on homoharringtonine or omacetaxine from 1970 to present. This article reviews the pharmacokinetics of OM and its clinical evolution for the treatment of CML pre- and post TKI development. Toxicity profile, drug administration and future directions are also discussed. EXPERT OPINION: OM represents a unique addition to the CML therapeutic armamentarium with its distinct mechanism of action and activity. The adverse event profile is manageable and with subcutaneous administration at the approved dose, cardiac toxicity is no longer a concern. The recent approval of home administration will facilitate access to this therapy and increase patient compliance. We conclude with specific scenarios where OM use should be considered in CP and AP-CML patients in the era of TKI therapy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".