Pharmacokinetic interaction study between eslicarbazepine acetate and lamotrigine in healthy subjects
Bibliographic record
Abstract
OBJECTIVE: Anti-epileptic drugs are often used in combination. Both eslicarbazepine (main metabolite of eslicarbazepine acetate, ESL) and lamotrigine undergo conjugation with glucuronic acid, and both eslicarbazepine and its glucuronide and lamotrigine glucuronide undergo extensive renal elimination; therefore, there is a potential for interaction. This study investigated the interaction between ESL and lamotrigine in healthy subjects. METHODS: Open-label study in two parallel groups of 16 healthy volunteers each. After an 8-day treatment with ESL or lamotrigine, ESL (1200 mg once-daily) and lamotrigine (150 mg once-daily) were co-administered for 19 days. Geometric mean ratios (GMR) and 90% confidence intervals (90% CI) for maximum plasma concentration (C(max)) and area under the plasma concentration-time curve in the dosing interval (AUC(0-24)) were calculated for eslicarbazepine (ESL active metabolite) and lamotrigine. RESULTS: The C(max) and AUC(0-24) GMR (90% CI) were, respectively, 95% (87-102%) and 96% (91-102%) for eslicarbazepine, and 88% (82-94%) and 86% (81-92%) for lamotrigine. The 90% CI of the C(max) and AUC(0-24) GMR fell within the prespecified acceptance interval (80-125%) both for eslicarbazepine and lamotrigine. CONCLUSION: There was no significant pharmacokinetic interaction between ESL and lamotrigine in healthy subjects. Therefore, no dosage adjustment appears to be usually required in either lamotrigine or ESL when the drugs are co-administered.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".