Population pharmacokinetics of olanzapine in children
Bibliographic record
Abstract
AIMS: The aim of this study was to evaluate the population pharmacokinetics (PopPK) of olanzapine in children and devise a model-informed paediatric dosing scheme. METHODS: The PopPK of olanzapine was characterized using opportunistically collected plasma samples from children receiving olanzapine per standard of care for any indication. A nonlinear mixed effect modelling approach was employed for model development using the software NONMEM (v7.4). Simulations from the developed PopPK model were used to devise a paediatric dosing scheme that targeted comparable plasma exposures to adolescents and adults. RESULTS: Forty-five participants contributed 83 plasma samples towards the analysis. The median (range) postnatal age and body weight of participants were 3.8 years (0.2-19.2) and 14.1 kg (4.2-111.7), respectively. The analysis was restricted to pharmacokinetic (PK) samples collected following enteral administration (oral and feeding tube). A one-compartment model with linear elimination provided an appropriate fit to the data. The final model included the covariates body weight and postmenstrual age (PMA) on apparent olanzapine clearance (CL/F). Typical CL/F and apparent volume of distribution (scaled to 70 kg) were 16.8 L/h (21% RSE) and 663 L (13% RSE), respectively. Developed dosing schemes used weight-normalized doses for children ≤6 months postnatal age or <15 kg and fixed doses for children ≥15 kg. CONCLUSION: We developed a paediatric PopPK model for enterally-administered olanzapine. To our knowledge, this analysis is the first study to characterize the PK of olanzapine in participants ranging from infants to adolescents. Body weight and PMA were identified as influential covariates for characterizing developmental changes in olanzapine apparent clearance.
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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 | 0.002 |
| 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 | 0.000 | 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".