Outcomes following exposure to drug interactions with ibrutinib in patients with chronic lymphocytic leukaemia
Bibliographic record
Abstract
Ibrutinib is metabolized by cytochrome P450 3A (CYP3A) and poses challenges with drug interactions. We conducted a population-based cohort study of Ontario residents aged ≥66 years who initiated ibrutinib for chronic lymphocytic leukaemia (CLL) to evaluate the frequency of potential drug interactions involving moderate/strong CYP3A inhibitors and inducers and their association with overall survival (OS). Secondary analyses employed a nested case-control design examining hospitalizations for haemorrhage or infection as potential markers of ibrutinib toxicity. Among 642 ibrutinib recipients (median age 74 years, 34.4% female), 70 (10.9%) received a concomitant CYP3A inducer while 404 (62.9%) received a concomitant CYP3A inhibitor. In the primary analysis, we found no association between death (n = 162) and concurrent use of either moderate/strong CYP3A inducers or inhibitors. In secondary analyses, 86 patients (13.4%) were admitted to hospital for bleeding and 287 patients (44.7%) for infection. Receipt of moderate/strong CYP3A inhibitors was associated with an increased odds of hospitalization for infection (odds ratio 2.88, 95% confidence intervals [CI] 1.29-6.43) but not haemorrhage. Among older patients receiving ibrutinib for CLL, concomitant use of CYP3A-modulating drugs is common. We found no association between use of interacting drugs and OS, but CYP3A inhibitors were strongly associated with hospitalization for infection, underscoring the importance of pharmacovigilance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".