Dose-Related Pharmacokinetics and Pharmacodynamics of Valproate in the Elderly
Bibliographic record
Abstract
Valproate exhibits a complex pharmacokinetic profile due to concentration-dependent protein binding and clearance. It has been shown that the protein binding of valproate decreases as the serum concentration increases in a young adult population. Furthermore, the percentage of protein binding is lower in the elderly compared with young adults at comparable low therapeutic serum concentrations. The extent of valproate protein binding at higher concentrations in the elderly has not been described. Studies conducted in the elderly have found unbound valproate clearance to be decreased compared with younger adults, although these changes in clearance have not been evaluated at higher therapeutic serum concentrations. We evaluated the pharmacokinetics of valproate (protein binding and clearance) across a wide dosage range in the elderly and measured the impact of this on drug-related side effects using a single-blind within-subject study design in 6 healthy elderly volunteers (aged 65-76 years). Steady-state total and unbound serum valproate concentrations were assessed at 3 doses: 500, 1000, and 1500 mg/d. As doses and valproate serum concentrations increased, the unbound fraction (10.0%, 13.0%, 17.4%) and total clearance (4.8, 6.0, 6.7 mL/h/kg) increased, respectively. Unbound clearance decreased (49.4, 45.8, 39.4 mL/h/kg) with increasing valproate serum concentrations. Drug-induced CNS effects and nausea severity scores correlated with total and unbound serum valproate concentrations. Significant dose-dependent changes in valproate pharmacokinetics were observed in the elderly.
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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 | 0.000 | 0.001 |
| 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 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".