Value of therapeutic drug monitoring of MMF therapy in pediatric transplantation*
Bibliographic record
Abstract
Therapeutic drug monitoring (TDM) is desirable whenever the desired drug effect cannot be predicted from a given dose, or when it is necessary to find a balance between the efficacy and toxicity of the drug. Children and adolescents particularly benefit from TDM, because dosing requirements are often not studied in the same detail as in adults. Also, drug-drug interactions are frequent. The gold standard for assessment of drug exposure is the area-under-the-curve (AUC) for a full pharmacokinetic profile. TDM for mycophenolic acid (MPA) is less well established. Monitoring of trough levels does not suffice because of enterohepatic recirculation of MPA after formation of its main metabolite, a glucoronide termed MPA-G. However, abbreviated sampling schemes specific to mycophenolate mofetil (MMF) correlate well with the AUC for MPA. Cyclosporine interacts with MPA by inhibiting the multidrug resistance-associated protein 2 (MRP2). Higher MPA concentrations result in a decreased two h concentration of cyclosporine, while higher cyclosporine exposure results in a lower MPA exposure. There are no drug interactions between tacrolimus and MPA, and lower doses of MMF are required in combination with tacrolimus. Steroids may induce the clearance of MPA, which could account in part for the increasing MPA exposure following transplantation. TDM has allowed for dosing recommendations of MMF in children, which could lead to improved efficacy and minimization of toxicities. It is important that these provisional target levels are validated in prospective studies. The above points clearly indicate that there is a role for TDM of MPA in pediatric transplant recipients.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".