Limited Sampling Strategy for the Estimation of Mycophenolic Acid and its Acyl Glucuronide Metabolite Area under the Concentration-Time Curve in Japanese Lung Transplant Recipients
Bibliographic record
Abstract
PURPOSE: The dose of mycophenolate mofetil (MMF) used to prevent rejection after lung transplantation is often adjusted based on the 12-hour area under the concentration-time curve (AUC0-12) of mycophenolic acid (MPA). A limited sampling strategy (LSS) is useful to define the pharmacokinetic (PK) profiles of MPA and mycophenolic acid acyl glucuronide (AcMPAG). Therefore, this study aimed to design a LSS based on multiple linear regression for estimating the AUC0-12 of MPA and AcMPAG at the minimum blood sampling points in Japanese lung transplant patients with concomitant tacrolimus. METHODS: Forty-five lung transplantation recipients were enrolled in a PK study of MPA, mycophenolic acid glucuronide (MPAG), and AcMPAG. The plasma MPA, MPAG, and AcMPAG concentrations were determined just before and at 0.5, 1, 2, 4, 8, and 12 hours after dosing. The AUC0-12 of MPA and AcMPAG was calculated using a linear trapezoidal rule from the plasma concentration of each blood sampling time. LSS was used to develop models for estimated AUC in the model group (n = 23) and was evaluated in the validation group (n = 22). RESULTS: The best three time-point equation was 4.04 + 1.64·C1 + 3.08·C4 + 5.17·C8 for MPA, and -0.13 + 3.01·C1 + 3.51·C4 + 5.74·C8 for AcMPAG. The prediction errors (PE) and the absolute prediction errors (APE) were within the clinically acceptable ± 5% and 15% range, respectively (MPA: PE = 2.00%, APE = 11.66%, AcMPAG: PE = 0.98%, APE = 14.69%). The percentage of estimated AUC0-12 within ± 15% of the observed AUC0-12 was 77.27% for MPA and 81.82% for AcMPAG. CONCLUSION: LSS using three time-point (C1, C4, and C8) provides the most reliable and accurate simultaneous estimation of the AUC0-12 of MPA and AcMPAG in Japanese lung transplant patients.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".