Limited Sampling Strategy to Predict AUC of the CYP3A Phenotyping Probe Midazolam in Adults: Application to Various Assay Techniques
Bibliographic record
Abstract
Midazolam clearance is used to phenotype hepatic CYP3A activity but requires multiple plasma samples following a single intravenous dose. The authors evaluated the use of a limited sampling scheme, using different assay techniques to determine the reproducibility of such a strategy in estimating midazolam AUC. Seventy-three healthy adults received midazolam as a single intravenous bolus dose. At least eight plasma samples were collected from each subject and were assayed using either LC/MS/MS or electron capture gas chromatography. Eleven subjects were randomly selected for the training set using stepwise linear regression to determine relationships between midazolam plasma concentrations and AUC. Validation of the predictive equations was done using the remaining 62 subjects. Mean percent error (MPE), mean absolute error (MAE), and root mean square error (RMSE) were calculated to determine bias and precision. Based on the training set, five models were generated with coefficients of determination ranging from 0.87 to 0.95. Validation showed that MPE, MAE, and RMSE values were acceptable for three of the models. Intrasubject reproducibility was good. In addition, training set datafrom one institution were able to predict data from the other two institutions using other assay techniques. Minimized plasma sampling mayprovide a simpler method for estimating midazolam AUC for CYP3A phenotyping. A limited sampling strategy is more convenient and cost-effective than standard sampling strategies and is applicable to more than one assay technique.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".