Population pharmacokinetic analysis of weekly and biweekly IgPro20 (Hizentra®) dosing in patients with primary immunodeficiency
Bibliographic record
Abstract
BACKGROUND: IgPro20 (Hizentra®), a 20% subcutaneous immunoglobulin G (IgG), is an effective treatment for patients with primary immunodeficiencies with impaired IgG production. Flexible dosing regimens of IgPro20 have been supported by pharmacokinetic (PK) modeling and simulation. This study further describes the PK characteristics of serum IgG concentrations after weekly and biweekly administration of IgPro20 and compares predicted and actual serum IgG data using a previously-developed population PK (popPK) model. METHODS: A popPK model was developed by combining data from a previously-published model with data from a Phase 4 study (IgPro20_4005). An external validation of the original model using dosing, demographics, and historic endogenous serum IgG concentrations from patients enrolled in study IgPro20_4005 was performed. This dataset was then simulated 300 times and predicted serum IgG PK characteristics compared with the observed data. RESULTS: A total of 173 patients (156 unique patients from original model and 17 patients from study IgPro20_4005) provided 4078 observations of serum IgG concentrations. The popPK estimates obtained demonstrated a clearance (% inter-individual variability) of 0.138 L/day (35%), volume of central compartment of 3.95 L (78.6%), inter-compartmental clearance of 0.260 L/day (56%), and volume of peripheral compartment of 4.44 L. Validation results indicated that observed serum IgG concentration vs time data fell within the 90% prediction intervals for median, 25th, and 75th percentiles of the simulated IgG concentration time courses. CONCLUSIONS: The present analysis validated the ability of the previously published popPK model to predict serum IgG concentration time profiles after biweekly subcutaneous IgPro20 administration.
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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 | 0.003 |
| 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".