Evaluating the Impact of a Switch to Nilotinib on Imatinib-Related Chronic Low-Grade Adverse Events in Patients With CML-CP: The ENRICH Study
Bibliographic record
Abstract
BACKGROUND: Many patients with chronic myeloid leukemia in chronic phase experience chronic treatment-related adverse events (AEs) during imatinib therapy. These AEs can impair quality of life and lead to reduced treatment adherence, which is associated with poor clinical outcomes. PATIENTS AND METHODS: In the phase II ENRICH (Exploring Nilotinib to Reduce Imatinib Related Chronic Adverse Events) study (N = 52), the effect of switching patients with imatinib-related chronic low-grade nonhematologic AEs from imatinib to nilotinib was evaluated. RESULTS: Three months after switching to nilotinib, 84.6% of the patients had overall improvement in imatinib-related AEs (primary endpoint). Of 210 imatinib-related AEs identified at baseline, 62.9% had resolved within 3 months of switching to nilotinib. Of evaluable patients, most had improvements in overall quality of life after switching to nilotinib. At screening, 65.4% of evaluable patients had a major molecular response (BCR-ABL1 ≤ 0.1% on the International Scale). After switching to nilotinib, the rate of the major molecular response was 76.1% at 3 months and 87.8% at 12 months. Treatment-emergent AEs reported with nilotinib were typically grade 1 or 2; however, some patients developed more serious AEs, and 8 patients discontinued nilotinib because of new or worsening AEs. CONCLUSION: Overall, results from the ENRICH study demonstrated that switching to nilotinib can mitigate imatinib-related chronic low-grade nonhematologic AEs in patients with chronic myeloid leukemia in chronic phase, in conjunction with acceptable safety and achievement of molecular responses. This trial was registered at www.clinicaltrials.gov as NCT00980018.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".