Limited Sampling Strategies for Predicting Area Under the Concentration-Time Curve of Mycophenolic Acid in Islet Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: Mycophenolate mofetil is widely used in islet transplant recipients and its active metabolite, mycophenolic acid (MPA), exhibits wide pharmacokinetic variability. However, to our knowledge, no limited sampling strategy (LSS) exists for monitoring MPA in this subpopulation. OBJECTIVE: To define optimal LSSs for MPA monitoring and to test their predictive performance in islet transplant recipients. METHODS: After written informed consent was obtained and upon administration of a steady-state morning mycophenolate mofetil dose, blood samples were collected at 0, 0.3, 0.6, 1, 1.5, 2, 3, 4, 6, 8, 10, and 12 hours from 16 stable islet transplant recipients. MPA concentrations were measured by a validated high-performance liquid chromatography method with ultraviolet detection and pharmacokinetic parameters analyzed by noncompartmental modeling. All 16 patients' profiles were used to develop the LSSs via multiple regression analysis. Potential LSSs were restricted to ones having R(2) 0.90 or greater and 3 or fewer time points within the first 4 hours postdose. Resulting equations were validated for their predictive performance using the jackknife method, with acceptable criteria for bias and precision preset to within +/-15%. In addition, 14 published LSSs (in the renal transplant population) were tested in our islet transplant patients. RESULTS: Five LSSs met preset criteria and had conventional sampling times: AUC = 1.783 + 1.248C1 + 0.888C2 + 8.027C4 (R2 = 0.98, bias = -3.09%, precision = 9.53%) AUC = 2.778 + 1.413C1 + 0.963C3 + 7.511C4 (R2 = 0.97, bias = -3.22%, precision = 11.02%) AUC = 1.448 + 1.239C1 + 0.271C1.5 + 9.108 C4 (R2 = 0.96, bias = -1.90%, precision = 11.46) AUC = 1.410 - 0.259C0 + 1.443C1 + 9.622C4 (R2 = 0.96, bias = -2.68%, precision = 11.53%) AUC = 1.547 + 1.417C1 + 9.448C4 (R2 = 0.96, bias = -2.46%, precision = 11.14%) where AUC = area under the concentration-time curve. None of the other published LSSs in the renal transplant population met the preset criteria for bias and precision. CONCLUSIONS: To our knowledge, these are the first precise and accurate LSSs for predicting MPA AUC developed specifically for islet transplant recipients. The LSS that we recommend is the one utilizing 2 concentrations: AUC = 1.547 + 1.417C1 + 9.448C4. This equation is convenient and clinically feasible. Other islet transplant centers may wish to validate our equation in their population or use our template as a guide to develop accurate and precise LSSs specific to their patient population.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".