Pharmacokinetics and Proposed Dosing of Levetiracetam in Children With Obesity
Bibliographic record
Abstract
OBJECTIVE: Characterize levetiracetam pharmacokinetics (PK) in children with obesity to inform dosing. METHODS: Children 2 to <21 years old receiving standard of care oral levetiracetam across two opportunistic studies provided blood samples. Levetiracetam plasma PK data were analyzed with a nonlinear mixed-effects modeling approach. Indirect measures for body size and covariates were tested for model inclusion. Individual empirical Bayesian estimates using the final model parameters were compared by obesity status. Monte Carlo simulation using total body weight was performed in children with normal estimated glomerular filtration rate to identify dosing for children with obesity that resulted in comparable exposures to normal weight adults and children after receiving label dosing. RESULTS: The population PK model was developed from 341 plasma concentrations from 169 children. A 1-compartment model best fit the data with fat-free mass as a significant covariate. Compared with children with normal weight, children with obesity had significantly lower body weight-normalized clearance (median [range], 4.77 [1.49-10.44] and 3.71 [0.86-13.55] L/h/70 kg, respectively). After label dosing with the oral formulation in children with obesity 4 to <16 years old, maximum and minimum steady-state concentrations were higher (25% and 41%, respectively [oral solution] and 27% and 19%, respectively [tablet]) compared with children with normal weight. Comparable exposures between children with and without obesity were achieved with weight-tiered dosing regimens of <75 kg or ≥75 kg. CONCLUSIONS: Weight-tiered dosing for levetiracetam oral solution and tablets for children with obesity 4 to <16 years old results in more comparable exposures to children of normal weight.
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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".