Lack of effect of ezogabine/retigabine on the pharmacokinetics of digoxin in healthy individuals: results from a drug–drug interaction study
Bibliographic record
Abstract
INTRODUCTION: The potential for ezogabine/retigabine (EZG/RTG) and its N-acetyl metabolite (NAMR) to inhibit the transporter protein P-glycoprotein-(P-gp)-mediated digoxin transport was tested in vitro. EZG/RTG did not inhibit P-gp. However, NAMR inhibited P-gp in a concentration-dependent manner. Based on these in vitro results, NAMR had the potential to inhibit P-gp at therapeutic doses of EZG/RTG (600-1,200 mg/day). As digoxin has a narrow therapeutic index, inhibition of digoxin clearance may have an impact on its safety. METHODS: An open-label, single-center, two session, fixed-sequence study was conducted to assess the effect of co-administration of therapeutic doses of EZG/RTG on digoxin pharmacokinetics in healthy adults. In session 1, subjects received a single dose of digoxin 0.25 mg. In session 2, EZG/RTG was up-titrated over 6 weeks. Digoxin 0.25 mg was co-administered at EZG/RTG steady-state doses of 600, 900, and, based on tolerability, 1,050/1,200 mg/day. Blood samples were collected over 144 hours for determination of digoxin, EZG/RTG, and NAMR concentrations. Urine samples were collected over 48 hours for determination of digoxin concentrations. RESULTS: Of 30 subjects enrolled, 29 were included in the pharmacokinetic analysis. Compared with digoxin alone, co-administration with EZG/RTG led to small increases in the digoxin plasma area under the concentration-time curve (AUC)0-120 at doses of 600, 900, and 1,050/1,200 mg (geometric mean ratio 1.08, 90% confidence interval [CI] 1.01-1.15; 1.18, 90% CI 1.10-1.27; 1.13, 90% CI 1.05-1.21, respectively). Safety was consistent with previous repeat-dose studies of EZG/RTG in healthy subjects. CONCLUSION: Co-administration of EZG/RTG across the therapeutic range resulted in small, non-dose-dependent and non-clinically relevant increases in digoxin systemic exposure, suggesting that digoxin dose adjustment is not necessary.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".