How Critical is the Duration of the Sampling Scheme for the Determination of Half-Life, Characterization of Exposure and Assessment of Bioequivalence?
Bibliographic record
Abstract
In noncompartmental analysis, poor characterization of the terminal elimination rate constant (Kel) will lead to biased results for half-life and total exposure (AUCinf), providing incorrect relative bioavailability and bioequivalence conclusions. We set out to determine if the sampling scheme duration was crucial for proper half-life and AUCinf determination. Profiles for 1000 subjects were simulated with a sampling scheme covering five half-lives. Concentrations were gradually removed from the end of the profile to determine if precision and bias in the half-life and AUCinf values were affected. Additionally, 30 bioequivalence studies were simulated to determine the influence of unreliable AUCinf PK parameter on BE conclusions. Precision and bias became unacceptable for AUCinf and half-life if Kel was not determined with a sampling scheme covering at least 2 and 4 half-lives, respectively. Bioequivalence conclusions also deteriorated if unreliable PK parameters were maintained. Sampling scheme duration is important when calculating noncompartmental parameters. In conclusion, sampling scheme duration should be at least 4 times the average measured half-life in order to have confidence in the reported half-life values. Additionally, individual subject's pharmacokinetic parameters should be removed from the pivotal statistical analysis when their associated calculated half-life is longer than half of the total sampling interval.
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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 | 0.076 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".