Asciminib remained superior vs bosutinib in late-line CML-CP after nearly 4 years of follow-up in ASCEMBL
Bibliographic record
Abstract
ABSTRACT: The efficacy of and disease control afforded by tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia in chronic phase (CML-CP) have led to increased longevity and thus the continued pursuit of alternative therapies that are efficacious and maximize tolerability. The 24- and 96-week analyses from ASCEMBL demonstrated superior efficacy, safety, and tolerability of asciminib when compared with bosutinib in later-line therapy, thereby meeting the primary and key secondary objectives. With nearly 4 years of follow-up, data from ASCEMBL continued to demonstrate the superior efficacy, safety, and tolerability of asciminib over bosutinib. At week 156, the major molecular response (MMR) rates remained higher with asciminib (33.8%) than with bosutinib (10.5%); the difference in MMR rates between arms, after adjusting for baseline major cytogenetic response, was 23.2% (95% confidence interval, 13.14-33.18; 2-sided P < .001). Asciminib continued to cause fewer grade ≥3 adverse events (AEs; 59.6% vs 68.4%) and fewer AEs that led to treatment discontinuation (8.3% vs 27.6%) than bosutinib. This updated analysis also includes patients who switched to asciminib because of a lack of efficacy with bosutinib. Two of the 25 patients who switched achieved MMR by the end of study, suggesting that earlier incorporation of asciminib, before other TKIs, may improve responses, albeit modestly. These long-term results further solidify asciminib as the therapy of choice for patients with CML-CP who were previously treated with ≥2 previous TKIs. This trial was registered at 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".