DIFFERENCES IN PHARMACOKINETIC PROFILES BETWEEN LONG-TERM LIVER AND KIDNEY/PANCREAS TRANSPLANT PATIENTS RECEIVING MAINTENANCE TACROLIMUS AND MYCOPHENOLATE MOFETIL
Bibliographic record
Abstract
P786 Therapeutic drug monitoring (TDM) of tacrolimus (Tacro) is based on trough level concentration (C0). However, either acute rejection or renal dysfunction may be observed in the presence of “therapeutic” Tacro C0 levels. TDM of mycophenolate mofetil (MMF) is usually based on side-effects and not on mycophenolic acid (MPA) C0. Aim: To determine the optimal time point which predicts Tacro and MPA AUC0-12 hr in long-term liver and kidney/pancreas transplant (Tx) patients (pts); and to apply this knowledge in pts experiencing either rejection or drug toxicity. Methods: We studied 14 long-term liver Tx pts (57±16 yrs) and 13 kidney/pancreas Tx pts (50±6 yrs) on maintenance Tacro and MMF. All the pts were stable, >1-yr post-Tx. The AUC0-12 hr was constructed from 8 blood samples per pt. Results: Results are shown in the tables. Table 1. Correlation (r2) between Tacro or MPA AUC 0–12 hr and single time points in long-term liver Tx ptsFigureTable 2. Correlation (r2) between Tacro or MPA AUC 0–12 hr and single time points in long-term kidney/pancreas Tx ptsFigureConclusion: Our results suggest that in both long-term liver and kidney/pancreas Tx pts, Tacro C4 and MPA C2 appear to be the best surrogates of Tacro and MPA AUC0-12 hr respectively. For practical reasons, Tacro C2 rather than C4 could be considered for TDM in long-term liver Tx pts. Tacro C0 remains a more convenient tool for TDM in long-term kidney/pancreas Tx pts. Prospective studies will determine whether MPA TDM in problem pts should be based on C2 or on the measurement of AUC0-12 hr.
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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 | 0.000 | 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 | 0.000 |
| 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".