Practical considerations in the management of patients treated with bosutinib for chronic myeloid leukemia
Bibliographic record
Abstract
Bosutinib is a second-generation tyrosine kinase inhibitor indicated for the treatment of patients with newly diagnosed Philadelphia chromosome-positive chronic phase chronic myeloid leukemia (CML), and for patients with Ph + chronic phase, accelerated phase, or blast phase CML resistant or intolerant to prior therapy. As is the case for all TKIs approved for treatment of CML, bosutinib is associated with adverse events (AEs) that require appropriate management to ensure adherence to treatment and optimized outcomes. The aim of this review is to provide physicians with updated practical information for the prevention and management of AEs occurring during treatment with bosutinib, including dosing strategies, based on the latest published evidence and clinical experience. Clinical studies and real-world evidence have shown bosutinib has a generally favorable safety profile, which has remained consistent across lines of therapy and in long-term reports. Adjusting the starting dose and/or modifying the dose during treatment with bosutinib are important strategies to manage AEs and improve tolerability, which are recognized within the label and in treatment guidelines. Dosing adjustment strategies to manage AEs are a recognized management approach for other TKIs in the treatment of CML and are not exclusive to bosutinib. In summary, long-term results from clinical trials and emerging real-world evidence demonstrate bosutinib has a safety profile that can largely be managed with treatment modifications and/or supportive care. Increased experience in managing toxicities and by using a personalized dosing approach may further improve adherence and outcomes with bosutinib.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".