Pharmacokinetics of apixaban and tacrolimus or cyclosporine in kidney and lung transplant recipients
Bibliographic record
Abstract
Abstract Apixaban is frequently used off‐label in transplant recipients. However, a potential drug interaction exists with the calcineurin inhibitors. We conducted an open‐label drug–drug interaction study to determine the pharmacokinetics of apixaban in lung and kidney transplant recipients who were taking a calcineurin inhibitor. A single dose of apixaban 10 mg was administered orally to kidney and lung transplant recipients maintained on either tacrolimus or cyclosporine, and pharmacokinetic parameters were compared to a reference cohort of 12 healthy subjects who used the same apixaban dose and pharmacokinetic blood sampling. Fourteen participants were enrolled ( n = 6 kidney, n = 8 lung), with 10 maintained on tacrolimus and four on cyclosporine. Data from 13 participants was usable. Participants were taking triple therapy immunosuppression and had a mean (SD) of 12 (3) medications. Participants receiving tacrolimus and cyclosporine had area under the plasma concentration–time curve from time zero to infinity (AUC 0‐inf ) geometric least square means (90% confidence interval [CI]) of 4312 (95% CI 3682, 5049) and 5388 (95% CI 3277, 8858), respectively. Compared to healthy subjects, the associated geometric mean ratios (GMRs) for apixaban maximum plasma concentration (C max ), AUC from time zero to the last quantifiable concentration (AUC 0‐tlast ) and AUC 0‐inf were 197% (95% CI 153, 295), 244% (95% CI 184, 323), and 224% (95% CI 170, 295) for transplant recipients on tacrolimus. The GMR (90% CI) C max , AUC 0‐tlast , and AUC 0‐inf of apixaban for patients on cyclosporine were 256% (95% CI 184, 358), 287% (95% CI 198, 415), and 280% (95% CI 195, 401). Kidney and lung transplant recipients receiving tacrolimus had higher apixaban exposure. A similar trend was noted for patients receiving cyclosporine, but additional patients are needed to confirm this interaction. Future studies are needed before apixaban can be safely recommended in this population, and the impact of dose staggering should be investigated. This study highlights the importance of pharmacokinetic studies in actual patient populations.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".