Population Pharmacokinetics of Nirsevimab in Preterm and Term Infants
Bibliographic record
Abstract
Nirsevimab, a monoclonal antibody with an extended half-life, is approved for the prevention of respiratory syncytial virus (RSV) disease in all infants in Canada, the EU, Great Britain, and the USA. A population pharmacokinetics (PK) model was built to describe the PK of nirsevimab in preterm and term infants, and to evaluate the influence of covariates, including body weight and age, in infants. Nirsevimab PK was characterized by a 2-compartment model with first-order clearance (CL) and first-order absorption following intramuscular (IM) administration. The typical CL in a 5 kg infant was 3.4 mL/day. Body weight and postmenstrual age were the primary covariates on CL, with minor effects for race, second RSV season, and antidrug antibody status (deemed not clinically relevant). Congenital heart disease (CHD) and chronic lung disease (CLD) did not significantly impact nirsevimab PK. The final population PK model, based on 8987 PK observations from 2683 participants across 5 clinical trials, successfully predicted PK in an additional cohort of 967 healthy infants. Weight-banded dosing (50 mg in infants <5 kg; 100 mg in infants ≥5 kg) was predicted to be appropriate for infants ≥1 kg in their first RSV season. Together, these data support weight-banded dosing of nirsevimab in all infants in their first RSV season, including in healthy infants, infants with CHD or CLD, and in infants born prematurely.
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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 | 0.005 |
| 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 | 0.000 |
| 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".