A phase 3, open-label, randomized study of asciminib, a STAMP inhibitor, vs bosutinib in CML after 2 or more prior TKIs
Bibliographic record
Abstract
Patients with chronic myeloid leukemia in chronic phase (CML-CP) resistant/intolerant to ≥2 tyrosine kinase inhibitors (TKIs) are at high risk of experiencing poor outcomes because of disease biology and inadequate efficacy and/or safety of current therapies. Asciminib, a first-in-class BCR-ABL1 inhibitor Specifically Targeting the ABL Myristoyl Pocket (STAMP), has the potential to overcome resistance/intolerance to approved TKIs. In this phase 3, open-label study, patients with CML-CP previously treated with ≥2 TKIs were randomized (2:1) to receive asciminib 40 mg twice daily vs bosutinib 500 mg once daily. Randomization was stratified by major cytogenetic response (MCyR) status at baseline. The primary objective was to compare the major molecular response (MMR) rate at week 24 for asciminib vs bosutinib. A total of 233 patients were randomized to asciminib (n = 157) or bosutinib (n = 76). Median follow-up was 14.9 months. The MMR rate at week 24 was 25.5% with asciminib and 13.2% with bosutinib. The difference in MMR rate between treatment arms, after adjusting for MCyR at baseline, was 12.2% (95% confidence interval, 2.19-22.30; 2-sided P = .029). Fewer grade ≥3 adverse events (50.6% vs 60.5%) and adverse events leading to treatment discontinuation (5.8% vs 21.1%) occurred with asciminib than with bosutinib. The study showed a superior efficacy of asciminib compared with that of bosutinib, together with a favorable safety profile. These results support the use of asciminib as a new therapy in patients with CML-CP who are resistant/intolerant to ≥2 prior TKIs. This trial was registered at www.clinicaltrials.gov as #NCT03106779.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| 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".