Physiologically Based Pharmacokinetic Modeling of Oxcarbazepine to Characterize Its Disposition in Children with Obesity
Bibliographic record
Abstract
Oxcarbazepine (OXC) is a second-generation antiseizure medication, effective through its active metabolite, 10-mono-hydroxy derivative (MHD). OXC is used as adjunctive therapy for focal-onset and primary generalized tonic-clonic seizures, with recommended dosing based on age and body weight. This study uses physiologically based pharmacokinetic (PBPK) modeling and leverages pharmacokinetic (PK) data acquired from children enrolled in pragmatic trials to understand dosing and subsequent exposure requirements in children with obesity. Drug concentrations of OXC and MHD (n = 148 each) from children with (n = 31) and without (n = 10) obesity, aged 2-20 years, were collected from two clinical trials (NCT01431326 and NCT02993861) and used for external evaluation of a previously developed PBPK model of OXC using PK-Sim. We used a previously published virtual population that accounts for the obesity-related changes in physiology (e.g., liver size and glomerular filtration rate) in children for PK simulations in children with obesity. Model evaluation showed that ≥80% of MHD concentrations contributed by about two thirds of study subjects (26 out of 41) fell within the 90% prediction interval. The PBPK model showed that children with obesity had lower median (interquartile range) simulated weight-normalized clearance (0.060 L/h/kg [0.048-0.076 L/h/kg]) than children without obesity (0.067 L/h/kg [0.060-0.077 L/h/kg]). Simulations revealed that the recommended pediatric dosing regimen produced comparable MHD exposure between children with and without obesity at steady state, supporting its applicability regardless of obesity status. This PBPK-based dosing aligns with product label recommendations and demonstrates the potential of PBPK modeling for dosing other drugs in children with obesity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".