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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| 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".