Are Tacrolimus Pharmacokinetics Affected by Nephrotic Stage?
Bibliographic record
Abstract
BACKGROUND: Although tacrolimus therapy is not the first-line therapy for childhood nephrotic syndrome, it is often used instead of cyclosporine to ameliorate the side effects. The pharmacokinetics (PK) of tacrolimus (Tac) can be influenced by many conditions, and it has a high plasma protein binding. The Tac PK during relapse and remission of childhood nephrotic syndrome has not been well described. METHODS: We performed 14 PK profiles (with measurements before intake and 0.5, 1, 2, 4, and 12 hours postintake) in 7 children with steroid-resistant nephrotic syndrome at week 1 (all nephrotic) and week 16 after Tac therapy (all in remission). These data were compared with historical PK data of 161 PK profiles in 87 pediatric renal transplant recipients with measurements before intake and 0.5, 1, 1.5, 2, 3, 4, 6, 8, and 12 hours postintake. Tac levels were measured using the Abbott Tacro II assay. We used descriptive statistics to generate percentiles and compared these with those of patients with steroid-resistant nephrotic syndrome. RESULTS: The median age of patients with nephrotic syndrome was 3.2 years (range 2.5, 17.2), male gender 71.4%, significantly younger than the control group. Median Tac dose was similar during both PK profiles (0.11 mg·kg·d at week 1 versus 0.13 mg·kg·d at week 16, P = 0.81). There were no statistically significant differences in median dose-normalized area-under-the-time-concentration profiles, peak concentration, time to reach peak concentration, and Tac trough levels. Individual dose-normalized Tac levels for each time point during the PK profile were also not different (P = 0.81). CONCLUSIONS: We conclude that Tac PK profiles are unaltered during relapse of nephrotic syndrome.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".