Pharmacokinetic modeling and simulation to understand diamorphine dose‐response in neonates, children, and adolescents
Bibliographic record
Abstract
Abstract Pharmacokinetic‐pharmacodynamic modeling and simulation can facilitate understanding and prediction of exposure‐response relationships in children with acute or chronic pain. The pharmacokinetics of diamorphine (diacetylmorphine, heroin), a strong opioid, remain poorly quantified in children and dose is often guided by clinical acumen. This tutorial demonstrates how a model to describe intranasal and intravenous diamorphine pharmacokinetics can be fashioned from a model for diamorphine disposition in adults and a model describing morphine disposition in children. Allometric scaling and maturation models were applied to clearances and volumes to account for differences in size and age between children and adults. The utility of modeling and simulation to gain insight into the analgesic exposure‐response relationship is demonstrated. The model explains reported observations, can be used for interrogation, interpolated to determine equianalgesia and inform future clinical studies. Simulation was used to illustrate how diamorphine is rapidly metabolized to morphine via its active metabolite 6‐monoacetylmorphine, which mediates an early dopaminergic response accountable for early euphoria. Morphine formation is then responsible for the slower, prolonged analgesic response. Time‐concentration profiles of diamorphine and its metabolites reflected disposition changes with age and were used to describe intravenous and intranasal dosing regimens. These indicated that morphine exposure in children after intranasal diamorphine 0.1 mg.kg −1 was similar to that after intranasal diamorphine 5 mg in adults. A target concentration of morphine 30 μg.L −1 can be achieved by a diamorphine intravenous infusion in neonates 14 μg.kg −1 .h −1 , in a 5‐year‐old child 42 μg.kg −1 .h −1 and in an 15 year‐old‐adolescent 33 μg.kg −1 .h −1 .
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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 | 0.001 |
| 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".