Use of <scp>physiologically‐based</scp> pharmacokinetic modeling to inform dosing of the opioid analgesics fentanyl and methadone in children with obesity
Bibliographic record
Abstract
Obesity is an increasingly alarming public health threat, with nearly 20% of children classified as obese in the United States today. Children with obesity are commonly prescribed the opioids fentanyl and methadone, and accurate dosing is critical to reducing the risk of serious adverse events associated with overexposure. However, pharmacokinetic studies in children with obesity are challenging to conduct, so there is limited information to guide fentanyl and methadone dosing in these children. To address this clinical knowledge gap, physiologically-based pharmacokinetic models of fentanyl and methadone were developed in adults and scaled to children with and without obesity to explore the interplay of obesity, age, and pharmacogenomics. These models included key obesity-induced changes in physiology and pharmacogenomic effects. Model predictions captured observed concentrations in children with obesity well, with an overall average fold error of 0.72 and 1.08 for fentanyl and methadone, respectively. Model simulations support a reduced fentanyl dose (1 vs. 2 μg/kg/h) starting at an earlier age (6 years) in virtual children with obesity, highlighting the importance of considering both age and obesity status when selecting an infusion rate most likely to achieve steady-state concentrations within the target range. Methadone dosing simulations highlight the importance of considering genotype in addition to obesity status when possible, as cytochrome P450 (CYP)2B6*6/*6 virtual children with obesity required half the dose to match the exposure of wildtype children without obesity. This physiologically-based pharmacokinetic modeling approach can be applied to explore dosing of other critical drugs in children with obesity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".