Regulatory and Study Conditions for the Determination of Bioequivalence of Highly Variable Drugs
Bibliographic record
Abstract
PURPOSE: The FDA Working Group on Highly Variable (HV) Drugs recently presented interim procedures and conditions for determining the bioequivalence (BE) of HV drug products. They included analysis by the method of scaled average BE (SABE), a switching coefficient of variation of CV S = 30% and a regulatory standardized variation of CV 0 = 25% for applying SABE, and the use of a secondary regulatory criterion restricting to 0.80-1.25 the point estimate for the ratio of estimated geometric means (GMR) of the two formulations. These conditions are scrutinized in the present communication. METHODS: 3-period BE studies were simulated with various statistical and regulatory assumptions. Power curves, obtained by gradually increasing the true GMR, compared performances of the methods of SABE, a constrained point estimate of GMR (PE/GMR), and the composite of these two approaches. The consumer risk of each procedure was evaluated. RESULTS: With CV 0 = 30% and PE/GMR = 0.80-1.25, the composite criterion of BE relied on the confidence limits of SABE. In contrast, with CV 0 = 25% and/or PE/GMR = 0.87-1.15, the composite criterion approached almost completely the features of the GMR point estimate, especially at high within-subject variation. The consumer risk was near 5% with CV 0 = 30% but much higher when CV 0 = 25%. CONCLUSIONS: A constraint on GMR is difficult to justify scientifically. Still, if it is introduced then the condition of CV S = CV 0 = 30% and PE/GMR = 0.80-1.25 is recommended as a composite regulatory criterion. With alternative settings of the conditions, such as the recommended CV 0 = 25% and/or PE/GMR = 0.87-1.15, the composite criterion would reflect almost entirely the GMR point estimate. Also, with CV 0 = 25%, the BE limits are discontinuous at CVS = 30% and, as consequences, the consumer risk is substantially larger than 5%, and the regulatory uncertainty for making a decision about acceptance or rejection is enhanced. These would be undesirable outcomes.
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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 | 0.057 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".