Physiologically Based and Population Pharmacokinetic Modeling of Midazolam in Children With Obesity Using Real‐World Data
Bibliographic record
Abstract
Children represent a highly complex and variable population for treatment, including interindividual differences in drug dose-exposure. Midazolam has been used as a sedative for hospitalized children on- and off-label; however, factors affecting interindividual variability (IIV) in observed clearance for this population are not fully understood and can result in extreme under- or overexposure. Obesity has been described as a significant influence on midazolam in adolescents, which could potentially alter drug exposure. The goal of this study was to use two modeling strategies to evaluate dose-exposure of midazolam in children with and without obesity. Population pharmacokinetic modeling assessed whether measures of obesity status would explain some of the observed IIV for midazolam clearance. In all, 164 plasma concentrations were collected from 93 participating children, many with obesity. Covariate modeling did not identify any factors influential to clearance beyond body weight. Model IIV was similar to that observed in previous models of critically ill children (coefficient of variation, 175%) along with considerable residual unexplained variability (50.4%). Then, a previously published virtual population of children with obesity was incorporated into an existing physiologically based pharmacokinetic model of midazolam in the open-source PK-Sim software. Dosing simulations for a subset of 46 participants demonstrated minor overpredictions in children with obesity compared to those without. Both models predicted a minor (< 20%) increase in exposure for children with obesity given the same weight-based dose. This research demonstrates the use of population pharmacokinetics combined with physiologically based pharmacokinetic modeling to compare simulated exposures in children with and without 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 | 0.001 | 0.005 |
| 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 | 0.001 | 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".