A Retrospective Study on Mycophenolic Acid Drug Interactions: Effect of Prednisone, Sirolimus, and Tacrolimus With MPA
Bibliographic record
Abstract
Mycophenolic acid (MPA), the active compound of mycophenolate mofetil (MMF), is widely used as an antirejection drug after renal transplantation. There is growing evidence supporting the notion that there is substantial variability in the intra- and interpatient exposure to MPA. Drug interactions involving MPA with tacrolimus, steroids, and sirolimus have been understudied. The objective of this study was to determine the relationship between MPA, steroids, tacrolimus, and sirolimus. MPA trough concentrations from 37 pediatric renal transplant recipients (mean age 7.6 years at transplant) followed for a median follow-up of 7.8 years were analyzed retrospectively and 2131 dose-normalized MPA trough concentrations were evaluated against all known covariates including all concomitant immunosuppressant drug doses and exposure, age, albumin, hematocrit, and estimated glomerular filtration rate (eGFR). Age, hematocrit, and estimated glomerular filtration rate affected the dose-normalized MPA trough concentrations. The authors used appropriate linear regression univariate models and created 5 different multivariate models to examine individual drug-drug interactions (DDIs). Although the authors' findings support the notion that there is a DDI between MMF and both sirolimus and steroids, the sample size was small, and these findings should be confirmed in future studies. The authors found no DDIs between tacrolimus and MMF, the prodrug of MPA. These findings are important because there is a tendency to under-dose MMF early and to overdose late after transplantation. The DDI between sirolimus and MMF has not been described. Although therapeutic drug monitoring of MMF therapy is often not performed, the data presented here indicate a necessity for therapeutic drug monitoring. This is especially true when converting from tacrolimus to sirolimus, as a way to avoid MPA underexposure and organ rejection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".