Single‐dose pharmacokinetics (PK) of ferric gluconate (FG) in iron‐deficient pediatric hemodialysis patients
Bibliographic record
Abstract
Purpose: Limited information exists on the use of any intravenous iron preparation in pediatric HD patients. This study was designed to describe the PK parameters of FG, now approved for use in children on HD. Methods:Iron‐deficient pediatric HD pts (≤15 yr) were randomized to 2 doses of FG. Blood samples taken during a 1 hr infusion and at intervals over 48 hrs were analyzed for total iron, transferrin‐bound iron (TBI), and FG‐bound iron (FGI). Results:49% of pts were male, 88% white, 57% age 6–12 yr, wt 16.3–63.2 Kg, ht 100–177.5 cm. Mean serum iron concentrations (total iron and FGI) rapidly increased in a dose‐dependent manner, approximately proportional to the FG dose administered. A rapid rise in total serum iron was followed by a slower, less prominent rise in TBI. Single‐dose PK of FGI was adequately described using non‐compartmental analytical methods. A standard 2‐compartment NONMEM model successfully fit the data and accurately described the time‐course of FGI concentrations. Pharmacokinetic Parameter 1.5 mg/kg FG (n = 22) 3.0 mg/kg FG (n = 26) Cmax(mean ± SD, mcg/dL) 1287 ± 285 2283 ± 637 AUC0–48(mean ± SD, mcg·hr/dL) 9327 ± 4038 16,830 ± 6526 AUC0–∞(mean ± SD, mcg·hr/dL) 9499 ± 4089 17,087 ± 6776 Tmax(mean ± SD, hrs) 1.1 ± 0.23 1.1 ± 0.19 t1/2(mean ± SD, hrs) 2.0 ± 0.7 2.5 ± 1.8 Kel(mean ± SD, hr−1) 0.43 ± 0.30 0.39 ± 0.27 Cl (mean ± SD, L/hr) 0.69 ± 0.50 0.66 ± 0.52 Vd(mean ± SD, L) 1.6 ± 0.6 1.9 ± 1.1 Conclusions: Cmax values in pediatric HD population were similar to Cmax values previously reported with similar FG doses in healthy iron‐deficient adults (Ferrlecit® prescribing information). In contrast, mean AUC0−∞ values were approximately 2 times greater, and mean Cl rates were 5.4 times slower in pediatric pts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".