Enzyme-inducing Anticonvulsants Increase Plasma Clearance of Dexmedetomidine
Bibliographic record
Abstract
BACKGROUND: Dexmedetomidine is useful during mapping of epileptic foci as it facilitates electrocorticography unlike most other anesthetic agents. Patients with seizure disorders taking enzyme-inducing anticonvulsants appear to be resistant to its sedative effects. The objective of the study was to compare the pharmacokinetic and pharmacodynamic profile of dexmedetomidine in healthy volunteers with volunteers with seizure disorders receiving enzyme-inducing anticonvulsant medications. METHODS: Dexmedetomidine was administered using a step-wise, computer-controlled infusion to healthy volunteers (n = 8) and volunteers with seizure disorders (n = 8) taking phenytoin or carbamazapine. Sedation and dexmedetomidine plasma levels were assessed at baseline, during the infusion steps, and after discontinuation of the infusion. Sedation was assessed by using the Observer's Assessment of Alertness/Sedation Scale, Ramsay Sedation Scale, and Visual Analog Scale and processed electroencephalography (entropy) monitoring. Pharmacokinetic analysis was performed on both groups, and differences between groups were determined using the standard two-stage approach. RESULTS: A two-compartment model was fit to dexmedetomidine concentration-time data. Dexmedetomidine plasma clearance was 43% higher in the seizure group compared with the control group (42.7 vs. 29.9 l/h; P = 0.007). In contrast, distributional clearance and the volume of distribution of the central and peripheral compartments were similar between the groups. No difference in sedation was detected between the two groups during a controlled range of target plasma concentrations. CONCLUSION: This study demonstrates that subjects with seizure disorders taking enzyme-inducing anticonvulsant medications have an increased plasma clearance of dexmedetomidine as compared with healthy control subjects.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".