Prediction of cyclosporine clearance in liver transplant recipients by the use of midazolam as a cytochrome P450 3A probe
Bibliographic record
Abstract
BACKGROUND: Interindividual differences in the kinetics of cyclosporine (INN, ciclosporin) result in part from variations in the activity of cytochrome P450 3A (CYP3A). The biotransformation of midazolam to 1'-hydroxymidazolam is also catalyzed by CYP3A. The objective of this study was to examine the usefulness of midazolam as a CYP3A probe to predict cyclosporine clearance. METHODS: Twenty-six stable liver transplant recipients receiving immunosuppressive therapy with oral cyclosporine (Neoral) were studied. Midazolam (0.015 mg/kg) was administered intravenously and a blood sample was obtained 1 hour later. The plasma concentration of midazolam and 1'-hydroxymidazolam was measured by gas chromatography-mass spectrometry. Blood concentration of cyclosporine was measured by a fluorescence polarization assay. Cyclosporine clearance was calculated as daily dose divided by trough level. RESULTS: There were large interindividual variations in cyclosporine clearance and in midazolam metabolism. Cyclosporine blood levels correlated poorly with dose (r = -0.016). However, there was a significant correlation between cyclosporine clearance and the plasma concentration of 1'-hydroxymidazolam (r = 0.559; P < .001) or the midazolam/1'-hydroxymidazolam plasma concentration ratio (r = 0.668; P < .001). CONCLUSION: Heterogeneity in CYP3A activity contributes to interpatient differences in cyclosporine dosage requirements after liver transplantation. Midazolam metabolism correlated with cyclosporine clearance, but it accounted for only about 40% of the variability in the apparent oral clearance of cyclosporine and this relationship is not tight enough to be useful in the prediction of cyclosporine dosage requirements in the clinical setting.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".