Limited Sampling Strategy for Mycophenolic Acid Area Under the Curve
Bibliographic record
Abstract
The immunosuppressive potential of mycophenolate mofetil (MMF), a prodrug for mycophenolic acid, is related to the area under the time-concentration curve (AUC). Full pharmacokinetic (PK) profiles are expensive and cumbersome, and therefore a limited sampling technique would be favorable. The authors retrospectively analyzed 114 full 10-point PK profiles from 61 pediatric patients receiving MMF. Stepwise multiple regression analysis was used to calculate limited sampling approaches. Correlation between AUC and predose trough concentration was r2 = 0.29 (p < 0.0001), and between AUC and postdose trough concentration was r2 = 0.48 (p < 0.0001). The best correlations were with the 2 hours (C2, r2 = 0.59, p < 0.0001), three hours (C3, r2 = 0.52, p < 0.0001), 1.5 hours (C1.5, r2 = 0.50, p < 0.0001), and six hours (C6 r2 = 0.43, p < 0.0001). No combination of any two sampling points resulted in significantly better correlation with AUC, but three-point estimates at C1, C2, and C6 resulted in excellent correlation between predicted AUC and AUC from the full profile when using the formula AUC = 10.75 + (0.98 x C1) + (2.38 x C2) + (4.86 x C6), Pearson r = 0.93, 95% confidence interval 0.89 to 0.95. Bland and Altman analysis revealed good agreement between predicted AUC and AUC from the full profile. The AUC of mycophenolic acid can be predicted by limited sampling including C1, C2, and C6.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".