Assessing Bioequivalence of Antiepileptic Drugs: Are the Current Requirements too Permissive?
Bibliographic record
Abstract
PURPOSE: In order to evaluate the permissiveness of current bioequivalence requirements for antiepileptic drugs we investigated how accurate Cmax and AUC0-t of generic antiepileptic drugs approved in Brazil are in comparison to reference products. METHODS: Data collected from assessment reports of approved bioequivalence studies archived in the Brazilian regulatory agency in 2007-2012 were: geometric mean ratios and 90% confidence intervals (CI) for Cmax and AUC0-t, intra-subject variability (CV) of Cmax and AUC0-t and number of subjects. RESULTS: The average difference in Cmax and AUC0-t between generic and reference products was 5% and 3%, respectively. Maximum deviation from 1.00 of the CI of Cmax can achieve 15-20% (demonstrated in 27% of studies); for AUC0-t, 25% of studies showed the deviation can be >10%. All studies that used adequate number of subjects for a 90% CI of 0.90-1.11 complied with it for AUC0-t, except one of carbamazepine, but only 33% complied with it for both AUC0-t and Cmax. The CV was strongly correlated to the maximum CI deviation for AUC0-t (CV of approximately 15% corresponding to deviation of 10%). Studies that presented maximum CI deviation ≤ 10 % together with CV ≤ 15% for AUC0-t represented 65% of the total. Weaker correlation was observed for Cmax and no correlation was seen between maximum CI deviation and number of subjects. CONCLUSIONS: Modification in legislation for bioequivalence of antiepileptic drugs is suggested, not only with constraint of AUC0-t 90% CI to 0.90-1.11, but also with limitation of the CV to 15%, as to assure similar variance in pharmacokinetics and diminish the risk of critical plasma-level fluctuation when switching between generic and reference formulations. Although most generics presented differences ≤ 10% in AUC0-t compared to their references, some narrow therapeutic index drugs displayed differences that could be clinically significant after product substitution.
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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 | 0.193 | 0.288 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 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".