Trapezoid bioequivalence: A rational bioavailability evaluation approach on account of the pharmaceutical‐driven balance of population average and variability
Bibliographic record
Abstract
Abstract Among the current approaches for the analysis of bioequivalence, the average bioequivalence (ABE) is limited only to the mean bioavailability, whereas the population bioequivalence (PBE) criterion aggregates both mean and variance in a general comparison formula. However, a rational bioequivalence criterion capable of judging specific drug considerations is always still preferred. As an alternative approach, we introduce an aggregate criterion, namely, the trapezoid bioequivalence (TBE), which includes the consideration of both mean and variance of the bioavailability and adapted weighting of a drug's therapeutic properties. We first applied our method to specific simulated scenarios to compare the strengths and weaknesses of current bioequivalence approaches and demonstrate the improvements brought by TBE. As well, the impact of sample size and variability on ABE, PBE, and TBE are assessed using a population pharmacokinetic model of methylphenidate. Our results indicate that TBE inherits the advantages of both ABE and PBE while greatly reducing their inadequacies. Through simulations with population pharmacokinetic models of specific scenarios, we confirm that (1) TBE does not encounter the overly permissiveness issue of PBE, (2) TBE respects the hierarchy to ABE (TBE => ABE), and (3) TBE assesses bioequivalence with a restriction on without an increase to type 2 errors. The clinically inspired simulations demonstrate TBE’s superiority in a realistic context and its potential usefulness in practice. Moreover, the parameter choice in TBE may be adapted according to the specific context of a drug's pharmacological and pharmacodynamic properties.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".