Pharmacokinetic modelling of intravenous immunoglobulin in children with primary immunodeficiencies and secondary antibody deficiencies
Bibliographic record
Abstract
Abstract Aims Children with primary immunodeficiency (PID) and secondary antibody deficiency (SAD) often require immunoglobulin replacement therapy due to low plasma immunoglobulin G (IgG) levels and recurrent infections. Existing pharmacokinetic models for immunoglobulin in PID patients predominantly focus on adults, with limited attention to secondary antibody deficiencies and a lesser emphasis on paediatric populations. This study aims to investigate the pharmacokinetic properties of IgG in paediatric patients with PID and SAD. Methods Population pharmacokinetic analysis for PID and SAD children treated with intravenous immunoglobulin at a tertiary paediatric centre was conducted using NONMEM® (7.5.1). Dosing simulations to achieve therapeutic levels of 6 and 8 gL −1 were performed. Results A population pharmacokinetic analysis of 64 patients (median age 4.08 years, range 0.06–16.8) was performed. A two‐compartment model with first‐order elimination, incorporating both additive and proportional residual error, adequately described the data. Interindividual variability was modelled on clearance, volume of distribution and baseline IgG levels, with allometric scaling to a 70‐kg body weight applied a priori. The estimated clearance was 0.308 L −1 day −1 70 kg −1 (95% CI 0.23, 0.67), and the volume of distribution was 10.96 L −1 70 kg −1 (95% CI 5.97, 15.79). Patients with SAD exhibited a lower clearance rate of 54% compared with PID patients. Dosing simulations indicated that the recommended SAD dosing regimen maintained therapeutic IgG levels in the simulated population. However, only 44.8% to 51.9% of patients with PID achieved target IgG levels with the standard regimen. Conclusions This study provides insights into immunoglobulin pharmacokinetics in paediatric PID and SAD patients, guiding optimised dosing strategies. Administering a loading dose would improve the probability of maintaining therapeutic IgG levels during the 4‐week dosing interval.
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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 | 0.001 | 0.000 |
| 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".