Generic products of antiepileptic drugs: A perspective on bioequivalence and interchangeability
Bibliographic record
Abstract
Most antiepileptic drugs (AEDs) are currently available as generic products, yet neurologists and patients are reluctant to switch to generics. Generic AEDs are regarded as bioequivalent to brand AEDs after meeting the average bioequivalence criteria; consequently, they are considered to be interchangeable with their respective brands without loss of efficacy and safety. According to the U.S. Food and Drug Administration (FDA) the present bioequivalence requirements are already so rigorous and constrained that there is little possibility that generics that meet regulatory bioequivalence criteria could lead to therapeutic problems. So is there a scientific rationale for the concerns about switching patients with epilepsy to bioequivalent generics? Herein we discuss the assessment of bioequivalence and propose a scaled-average bioequivalence approach where scaling of bioequivalence is carried out based on brand lot-to-lot variance as an alternative to the conventional bioequivalence test as a means to determine whether switching patients to generic formulations, or vice versa, is a safe and effective therapeutic option. Meeting the proposed scaled-average bioequivalence requirements will ensure that when an individual patient is switched, he or she has fluctuations in plasma levels similar to those from lot-to-lot of the brand reference levels and thus should make these generic products safely switchable without change in efficacy and safety 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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".