Are the current bioequivalence standards sufficient for the acceptance of narrow therapeutic index drugs? Utilization of a computer simulated warfarin bioequivalence model.
Bibliographic record
Abstract
PURPOSE: The purpose of this computer simulation was to determine the likelihood of two bioequivalent (vs. reference) generic warfarin formulations (with varying bioavailability) passing current bioequivalence criteria against each other at varying bioavailability. Methods. A bioequivalence simulation program generated 100 warfarin bioequivalence (BE) studies with 24 patients/study. The reference formulation (R) was assigned a bioavailability of 90%. In these simulations the first generic (G(1)) had a bioavailability that was incrementally decreased from 90%. The second generic (G(2)) had a bioavailability that was incrementally increased from 90%. The bioequivalence testing was performed initially as G(1 )vs. R, then G(2) vs. R, and finally G(2) vs. G(1). The tests were performed according to current criteria for therapeutic index drugs. RESULTS: 5400 BE studies with a total of 129,600 subjects and 2,462,400 sampling times were simulated. When G(1) vs. R was compared, fewer than 80% of studies passed when the relative AUC(0-t )ratios were 88% or less. When G(2) vs. R were compared, fewer than 80% of studies passed when the relative AUC(0-t )ratios were 113% or greater. When Generic 2 and Generic 1 were compared fewer than 80% of studies passed when the relative AUC(0-t) ratios deviated from the reference by 7% or more. DISCUSSION Despite limitations this simulation indicates that two bioequivalent (vs. reference) generic warfarin products may not be bioequivalent to each other. Alternative methods of assessing bioequivalence are needed when more than one generic of narrow therapeutic index drug exists on the market.
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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 | 0.008 | 0.043 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".