Effects of ibrutinib on in vitro platelet aggregation in blood samples from healthy donors and donors with platelet dysfunction
Bibliographic record
Abstract
Background: Ibrutinib, a first-in-class, once-daily inhibitor of Bruton's tyrosine kinase (BTK), is approved in the US and EU for the treatment of various B-cell malignancies. In clinical studies, BTK inhibitors have been associated with increased bleeding risk, which may result from BTK inhibition in platelets.Methods: To better understand the mechanism of ibrutinib in bleeding events, we isolated platelet-rich plasma from healthy donors (n = 8) and donors with conditions associated with impaired platelet function or with potentially increased bleeding risk (on hemodialysis, taking aspirin, or taking warfarin; n = 8 each cohort) and used light transmission aggregometry to assess platelet aggregation in vitro after exposure to escalating concentrations of ibrutinib, spanning and exceeding the pharmacologic range of clinical exposure.Results: Platelet aggregation was induced by agonists of 5 major platelet receptors: adenosine diphosphate (ADP), thrombin receptor-activating peptide 6 (TRAP6), ristocetin, collagen, or arachidonic acid (AA). Platelet aggregation induced by ADP, TRAP6, ristocetin, and AA was not meaningfully inhibited by the maximal concentrations of ibrutinib (10 µM). In contrast, collagen-induced platelet aggregation was dose-dependently inhibited by ibrutinib in all donor cohorts (maximum aggregation % with 10 μM ibrutinib, −64% to −83% of agonist activity compared to control agonist samples but without ibrutinib).Conclusion: These results confirm prior reports and support a mechanistic role for the inhibition of collagen-induced platelet aggregation in bleeding events among susceptible individuals receiving ibrutinib therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".