Response‐Based Dosing for Ponatinib: Model‐Based Analyses of the Dose‐Ranging OPTIC Study
Bibliographic record
Abstract
Optimizing Ponatinib Treatment in CP‐CML (OPTIC) was a randomized, phase II dose‐optimization trial of ponatinib in chronic phase‐chronic myeloid leukemia (CP‐CML) resistant to ≥ 2 tyrosine kinase inhibitors or with T315I mutation. Patients were randomized to starting doses of 45‐, 30‐, or 15‐mg ponatinib once daily. Patients receiving 45‐ or 30‐mg reduced to 15‐mg upon achievement of ≤ 1% BCR::ABL1 IS (≥ molecular response with 2‐log reduction (MR2)). The exposure‐molecular response relationship was described using a four‐state, discrete‐time Markov model. Time‐to‐event models were used to characterize the relationship between exposure and arterial occlusive events (AOEs), grade ≥ 3 neutropenia, and thrombocytopenia. Increasing systemic exposures were associated with increasing probability of transitioning from no response to ≥ MR1, and from MR1 to ≥ MR1, with odds ratios of 1.63 (95% confidence interval (CI), 1.06–2.73) and 2.05 (95% CI, 1.53–2.89) for a 15‐mg dose increase, respectively. Ponatinib exposure was a significant predictor of AOEs (hazard ratio (HR) 2.05, 95% CI, 1.43–2.93, for a 15‐mg dose increase). In the exposure‐safety models for neutropenia and thrombocytopenia, exposure was a significant predictor of grade ≥ 3 thrombocytopenia (HR 1.31, 95% CI, 1.05–1.64, for a 15‐mg dose increase). Model‐based simulations predicted a clinically meaningful higher rate of ≥ MR2 response at 12 months for the 45‐mg starting dose (40.4%) vs. 30‐mg (34%) and 15‐mg (25.2%). The exposure–response analyses supported a ponatinib starting dose of 45 mg with reduction to 15 mg at response for patients with CP‐CML.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".