Dose modification dynamics of ponatinib in patients with chronic-phase chronic myeloid leukemia (CP-CML) from the PACE and OPTIC trials
Bibliographic record
Abstract
Abstract Ponatinib, the only approved all known-BCR::ABL1 inhibitor, is a third-generation tyrosine-kinase inhibitor (TKI) designed to inhibit BCR::ABL1 with or without any single resistance mutation, including T315I, and induced robust and durable responses at 45 mg/day in patients with CP-CML resistant to second-generation TKIs in the PACE trial. However, cardiovascular toxicities, including arterial occlusive events (AOEs), have emerged as treatment-related AEs within this class of TKIs. The OPTIC trial evaluated the efficacy and safety of ponatinib using a novel, response-based, dose-reduction strategy in patients with CP-CML whose disease is resistant to ≥2 TKIs or who harbor T315I. To assess the dose-response relationship and the effect on the safety of ponatinib, we examined the outcomes of patients with CP-CML enrolled in PACE and OPTIC who received 45 mg/day of ponatinib. A propensity score analysis was used to evaluate AOEs across both trials. Survival rates and median time to achieve ≤1% BCR::ABL1 IS in OPTIC were similar or better than in PACE. The outcomes of patients with T315I mutations were robust in both trials. Patients in OPTIC had a lower exposure-adjusted incidence of AOEs compared with those in PACE. This analysis demonstrates that response-based dosing for ponatinib improves treatment tolerance and mitigates cardiovascular risk.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".