Effects of Renal Impairment on the Pharmacokinetics of Once-Daily Amantadine Extended-Release Tablets
Bibliographic record
Abstract
BACKGROUND: An extended-release formulation of amantadine (Osmolex ER™, Osmotica Pharmaceutical US LLC) was approved in February 2018 to treat Parkinson's disease and drug-induced extrapyramidal reactions in adults. OBJECTIVES: To determine the pharmacokinetic profile of extended-release amantadine in subjects with varying degrees of renal impairment. METHODS: ) received a single 129-mg dose (160 mg amantadine hydrochloride) of extended-release amantadine. Blood and urine samples for pharmacokinetic analysis were taken at scheduled intervals. A two-compartment pharmacokinetic population model was employed to determine optimum extended-release amantadine dosing in subjects with renal impairment. RESULTS: Following a single oral dose of the 129-mg extended-release amantadine tablet, amantadine plasma concentration increased slowly, reaching a peak at approximately 11 h. Amantadine elimination was reduced in subjects with renal impairment. Renal clearance decreased from 10,965 to 2618 mL/h in subjects with severe renal impairment compared to those with normal renal function. Pharmacokinetic modeling and simulation methods were used to recommend the oral administration of 129-mg extended-release amantadine tablets at intervals of 24, 48, 72, 96, 120, or 168 h depending on the degree of renal function. CONCLUSIONS: Renal impairment was associated with reduced amantadine clearance. Based on pharmacokinetic modeling and simulations, dose regimens were recommended for subjects with impaired renal function to provide systemic amantadine exposure similar to subjects with normal renal function taking a once-daily extended-release amantadine tablet.
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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) | 0.000 | 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) | 0.001 | 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".