Modifications of the Method for Calculating Absolute Drug Bioavailability
Bibliographic record
Abstract
PURPOSE: Absolute bioavailability (F) is calculated as the ratio of the area under the plasma drug concentration-time curve (AUC) between extravascular administration and intravenous injection. However, as distribution of a drug after intravenous administration does not reach an equilibrium in the body during the distribution phase, the plasma drug concentration at this phase does not reflect the total amount of drug in the body. The goal of this paper was to analyze the insufficiencies of the method for calculating on absolute bioavailability and to propose a modification to improve the calculation. METHODS: Literature reporting absolute bioavailability published during 1983-2014 was searched for ten drug candidates. Plasma drug concentrations representing the amount of drug in the body were then calculated at each time point during the distribution phase according to the plasma drug concentration-time relationship during the elimination phase. RESULTS: The AUC values based on the distribution equilibrium drug concentrations following intravenous injection were 75%±11% of the actually measured drug concentrations in the literature. The absolute bioavailability values in the literature were 76%±12% of the actual bioavailability based on the AUCs from distribution-equilibrium drug concentrations. CONCLUSIONS: The present method underestimates the absolute drug bioavailability and should be modified to represent the data more accurately. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".