Scaling or wider bioequivalence limits for highly variable drugs and for the special case of Cmax
Bibliographic record
Abstract
OBJECTIVE: To illustrate that bioequivalence (BE) can be effectively evaluated for highly variable (HV) drugs and drug products and for the special case of C(max) by using average BE. To demonstrate that either scaling or wider regulatory limits need not result in large observed ratios of the geometric means (GMR) of the 2 drug products. METHODS: Two- and 4-period crossover BE investigations with 24 subjects were simulated. Variabilities of 15, 25 or 35% were assumed in special studies of C(max) and 40% in the general investigations of HV drugs. Acceptance of BE was analyzed in each study by various procedures and regulatory criteria. Under each condition, the percentage of simulated investigations accepting BE was recorded as the simulated GMR was gradually raised from 1.00. RESULTS: Scaled average BE for HV drugs (in both 2- and 4-period studies) and expanding limits for C(max) increased substantially, as expected, the proportion of investigations accepting BE. An additional secondary regulatory criterion constrained the simulated GMR to 1.25 and limited the possibility of large deviations between the mean metrics of the 2 formulations. Acceptance of BE by the composite regulatory expectation never exceeded the acceptances by the separate component criteria. CONCLUSIONS: The sample size required for the evaluation of BE for HV drugs and drug products can be substantially reduced by applying the approach of scaled average BE. The same conclusion is reached from the determination of BE for the C(max) metric by expanding the regulatory limits to 0.75 - 1.33 or even to 0.70 - 1.43. Concerns for observations of high GMR values can be eased by imposing constraints with a secondary regulatory criterion.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".