Exploring various measures of the area under the curve for the assessment of dose-proportionality and estimation of bioavailability
Bibliographic record
Abstract
OBJECTIVE: In pharmacokinetics, the area under the concentration versus time curve (AUC) extrapolated to infinity (AUC0-∞) is the preferred metric but it is not always possible to have a reliable estimate of the terminal phase half-life. Here we sought to explore the accuracy of three different area measures to accurately identify dose proportionality and bioavailability. METHODS: One to three compartment model simulations with different doses for dose-proportionality or different rates and/or extents of bioavailability. Area measures evaluated were AUC0-∞, to the last quantifiable concentration (AUCtlast), and to a common time value (AUCt'). RESULTS: Under linear pharmacokinetics, AUCt' provided the most accurate measure of dose proportionality. Except for the one compartment model where AUC0-∞ provided the best predictor of the true measure, there was no clear advantage to the use of either of the three measures of AUC. CONCLUSION: With uncertainty about the terminal phase half-life, the use of AUCt' can be a very useful and even the preferred measure of exposure for use in assessing proportionality in exposure between doses. The choice of AUC measure in bioavailability is less clear and may depend on compartmental nature of the drug, and study parameters including assay sensitivity and sampling protocols.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".