Population pharmacokinetics of levocetirizine in very young children: the pediatricians’ perspective
Bibliographic record
Abstract
Developmental changes during infancy and childhood can affect drug pharmacokinetics (PK), i.e., absorption, distribution, metabolism, and renal excretion. This, in turn, influences optimal dosing, efficacy, and safety. To date, of the 40 H1-antihistamines available worldwide, only 11 have been studied in children using a PK approach. Here, we provide the pediatricians' perspective on the population PK of levocetirizine, the pharmacologically active enantiomer of cetirizine, in very young children who received oral cetirizine, and describe the factors that influence levocetirizine PK in this population. In a prospective, randomized, double-blind, parallel-group, placebo-controlled study, very young children received oral cetirizine 0.25 mg/kg twice daily for 18 months. Plasma levocetirizine concentrations were measured in timed, sparse blood samples collected at steady-state (3, 12 and 18 months after commencement of treatment) for the purpose of monitoring safety, and levocetirizine population, PK parameters were derived by using non-linear mixed effects modeling. In 343 children (age 14-46 months, body weight 8.2-20.5 kg), a total of 943 blood samples were obtained. Compliance with cetirizine dosing was documented. The population PK model used predicted that with increasing body weight, levocetirizine oral clearance would increase by 0.044 l/h/kg, and levocetirizine volume of distribution would increase by 0.639 l/kg. Levocetirizine PK were not influenced by eosinophilia, sensitization to allergens, allergic disease, gastroenteritis/diarrhea, or concomitant ingestion of other medications. This population PK model predicts that in very young children, the oral clearance of levocetirizine will be rapid and will increase as body weight and age increase, therefore, levocetirizine dosing should be based on body weight and age in this population. Compared with older patients, on a mg/kg basis, relatively higher doses may be needed, and twice-daily dosing may be necessary, as previously reported for the related racemic H1-antihistamine cetirizine.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".