A Review of the Therapeutic Role of Bosutinib in Chronic Myeloid Leukemia
Bibliographic record
Abstract
The development of the BCR::ABL1 tyrosine kinase inhibitors (TKIs) has transformed Philadelphia chromosome (Ph)-positive chronic myeloid leukemia (CML) from a fatal disease to an often-indolent illness that, when managed effectively, can restore a life expectancy close to that of the normal population. Bosutinib is a second-generation TKI approved for adults with Ph-positive CML in chronic phase, accelerated phase, or blast phase that is resistant or intolerant to prior therapy, and for newly diagnosed Ph-positive chronic phase CML. This review details the efficacy of bosutinib for the treatment of CML in the first- and second-line settings, as well as in third- and later-line settings for high-risk patients resistant or intolerant to at least 2 TKIs. It also outlines bosutinib studies that provide evidence for dose-optimization strategies that can be used to improve efficacy and effectively manage adverse events. The studies that provide evidence for specific patient populations benefiting particularly from bosutinib dose-optimization strategies are also discussed. The well-established, long-term side-effect profile and the potential to make dose adjustments with bosutinib make it an appropriate treatment option for patients with CML. Bosutinib has demonstrated a positive impact on health-related quality of life and an important role in the long-term treatment of patients with CML.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".