#1878 Amikacin dosing in pediatrics: virtual population insights into avoiding nephrotoxicity
Bibliographic record
Abstract
Abstract Background and Aims Aminoglycosides, such as Amikacin, are the cornerstone to treat severe infections in pediatric patients. Amikacin dosing requires achieving high peak concentrations to ensure efficacy against bacterial strains (up to > 60 mg/L depending on the strain) while maintaining trough levels < 2.5 mg/L to minimize nephrotoxic risks [1]. The minimum effective dose and frequency has been a debate due to the challenges of predicting ontogeny physiological changes. Current dosing schemes often miss peak and trough target levels in pediatric populations [1]. We present an age-stratified in-silico study to evaluate Amikacin dosing strategies in pediatric patients aged 2 to 24 months, focusing on optimizing peak and trough levels within a 24-hour dosing interval. Method An in-silico pharmacokinetic (PK) model based on [2] was validated against the clinical study results in [1]. Age-stratified virtual pediatric populations (2–24 months) were generated to account for maturational changes in GFR, weight, and height age-dependence based on growth charts and GFR data derived from (51)Cr-EDTA clearance measurements using a single blood sample method [3, 4]. Virtual populations were created for 2-, 6-, 12-, and 24-month-old cohorts (Table 1). The body surface area was computed using Mosteller's formula. Simulations in Python were conducted to evaluate Amikacin dosing strategies with 24-hour dosing intervals over 5 days of treatment. Results Using the standard maximal dosing scheme of 30 mg/kg once daily [1], the simulation shows that the fraction of patients with trough levels > 2.5 mg/L after 24 hours was 61.3%, 19.7%, 4.7% and 1.1% for the 2-, 6-, 12-, and 24-month age cohorts, respectively. Simulations using an optimized 24-hours dosing scheme, designed to maximize peak levels while maintaining trough levels below 2.5 mg/L, revealed a marked age-dependent difference in peak concentrations (Fig. 1). Median peak concentrations after initial administration were 42.5 mg/L, 48.9 mg/L, 50.9 mg/L, and 52.0 mg/L for the 2-, 6-, 12-, and 24-month age cohorts, respectively. A fraction of 6.2% of 2-month-olds and 18.8% of 24-month-olds reached peak levels >60 mg/L after the initial administration. Peak concentrations declined progressively across all age groups over the treatment days. The median peak concentration decreased by 56.4% for the 2-month-olds and 13.8% for the 24-month-olds by day 5 of treatment. Moreover, by day 5, none of the 2-month-olds and 11.4% of the 24-month-olds reached peak concentrations >60 mg/L. Conclusion Further dosage stratification of Amikacin by age is necessary in infants due to physiological kidney maturation and other developmental changes. High inter-patient variability emphasizes the need for therapeutic drug monitoring and dose adjustments during the treatment to optimize efficacy while minimizing the risk of nephrotoxicity.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".