Exposure–response analysis reveals that clinically important toxicity difference can exist between bioequivalent carbamazepine tablets
Bibliographic record
Abstract
WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • The occurrence of central nervous system‐related adverse effects has been apparently related to the absorption rate of carbamazepine (CBZ). • However, the differing absorption rate metrics of four carbamazepine formulations in a bioequivalence study were unclearly associated with the incidence of adverse effects. WHAT THIS STUDY ADDS • The relationship between the incidence of most neurological adverse effects and the absorption rate of CBZ was quantitatively established. • A mixed‐effect PK–PD model demonstrated the rapid development of acute tolerance to these effects. • Characterization of PD and PK–PD sensitivities showed that clinically significant differences in toxicity can exist between bioequivalent CBZ formulations. AIMS To assess whether, using the current regulatory criteria, therapeutically important differences can exist between bioequivalent carbamazepine (CBZ) tablets. A secondary goal was to demonstrate quantitatively the relationship between the risk of neurological adverse effects to orally ingested CBZ and the rate of absorption. METHODS Results of a bioequivalence study by Olling et al. (Biopharm Drug Dispos 1999; 20: 19–28) were reanalysed. Following an exploratory data analysis step, a mixed‐effect pharmacokinetic–pharmacodynamic (PK–PD) model was built to describe the dependence of adverse events on the CBZ concentration. RESULTS Rapid development of tolerance was demonstrated for most neurological adverse effects, with a characteristic half‐life of 02.29 h and an initial EC50 of 2.33 mg l −1 . The resulting tolerance PK–PD model was characterized further using the tools and terminology of sensitivity analysis. It was demonstrated that the maximum concentration ( C max ) exhibits poor PK and PD sensitivities, and that clinically significant differences can exist between formulations which otherwise comply with the bioequivalence requirements. In contrast, another PK metric, the partial AUC, was a much better marker of the early neurological adverse events observable during the absorption phase of the drug. CONCLUSIONS In clinical and regulatory considerations, the development of acute tolerance for adverse effects of CBZ must be taken into account. Partial AUC reflects more sensitively the risk of adverse events than C max . Instead of the current trend of tightening of the bioequivalence criteria for narrow therapeutic index drugs, the use of alternative, more sensitive PK metrics is proposed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| 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 teacher head, 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".