A Clinical Pharmacokinetic Study of Tacrolimus and Sirolimus Combination Immunosuppression Comparing Simultaneous to Separated Administration
Bibliographic record
Abstract
The pharmacokinetic (PK) interaction between tacrolimus (TAC) and sirolimus (SRL), similarly structured immunosuppressive compounds that share binding proteins, is unknown. The combination of SRL with cyclosporin (CsA) has been studied, and a 4-hour interval between dosing of the two drugs is recommended even though it is inconvenient for patients and may affect compliance. Twenty-five liver and kidney-pancreas transplant recipients treated with a combination of SRL and low-dose TAC completed full PK studies while being treated with 4-hour interval dosing (ID) and then with simultaneous dosing. Whole blood was sampled for immunoassay measurement of TAC and SRL levels. Blood concentration/dose ratios of SRL and TAC varied between patients by a factor of 8 and 5, respectively, but correlation between trough concentration levels (C(0)) and drug exposure area under the concentration-time curve (AUC) was excellent (TAC: r(2) = 0.82; SRL: r(2) = 0.83). Neither PK profiles of SRL nor those of TAC were altered by simultaneous administration. Dose-corrected AUC and C0 of TAC correlated with SRL (r(2) = 0.8 and 0.8, respectively). Bone marrow suppression and nephrotoxicity were not enhanced nor were any new toxicities observed when TAC and SRL were used in combination. These data confirm that simultaneous dosing of TAC and SRL after transplantation is safe and that trough level monitoring is adequate to control 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".