The Utility of the Population Approach Applied to Bioequivalence in Patients
Bibliographic record
Abstract
Mixed-effect modeling was used to compare the population pharmacokinetics of 2 formulations of cyclosporine in patients. An open-label, multicenter, conversion study in stable, 6-month post-renal allograft recipients was conducted to compare the safety and pharmacokinetics of oral Pliva Cyclosporine Soft Gelatin Capsules (USP Modified) with Neoral (cyclosporine soft gelatin capsules, USP Modified) in stable post-renal transplant patients. Blood samples were collected predose and for 12 hours postdose on days 1, 14, 15, 28, and 29. Whole-blood samples were analyzed for cyclosporine using high-performance liquid chromatography and mass spectroscopy. Estimates of pharmacokinetic parameters were generated using noncompartmental and population compartmental pharmacokinetic analysis. Moreover, the effects of demographic factors on the pharmacokinetics of cyclosporine were evaluated using the nonlinear mixed-effects modeling program NONMEM. The rate and extent of bioavailability of cyclosporine did not differ between Pliva Cyclosporine Soft Gelatin Capsules and Neoral. In the final model, gender and actual body weight significantly affected the central and peripheral volumes of distribution. In addition, the pharmacokinetics of cyclosporine was defined robustly in this patient population using population pharmacokinetic approaches. Results indicate that the Pliva Cyclosporine Soft Gelatin Capsules and Neoral are bioequivalent when administered to renal transplant patients. Pliva Cyclosporine Soft Gelatin Capsules can then be substituted for Neoral in stabilized patients without anticipating dose adjustments.
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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 | 0.050 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".