Population pharmacokinetics and humanized dosage regimens matching the peak, area, trough, and range of amikacin plasma concentrations in immune-competent murine bloodstream and lung infection models
Bibliographic record
Abstract
ABSTRACT Amikacin is an FDA-approved aminoglycoside antibiotic that is commonly used. However, validated dosage regimens that achieve clinically relevant exposure profiles in mice are lacking. We aimed to design and validate humanized dosage regimens for amikacin in immune-competent murine bloodstream and lung infection models of Acinetobacter baumannii . Plasma and lung epithelial lining fluid (ELF) concentrations after single subcutaneous doses of 1.37, 13.7, and 137 mg/kg of body weight were simultaneously modeled via population pharmacokinetics. Then, humanized amikacin dosage regimens in mice were designed and prospectively validated to match the peak, area, trough, and range of plasma concentration profiles in critically ill patients (clinical dose: 25–30 mg/kg of body weight). The pharmacokinetics of amikacin were linear, with a clearance of 9.93 mL/h in both infection models after a single dose. However, the volume of distribution differed between models, resulting in an elimination half-life of 48 min for the bloodstream and 36 min for the lung model. The drug exposure in ELF was 72.7% compared to that in plasma. After multiple q6h dosing, clearance decreased by ~80% from the first (7.35 mL/h) to the last two dosing intervals (~1.50 mL/h) in the bloodstream model. Likewise, clearance decreased by 41% from 7.44 to 4.39 mL/h in the lung model. The humanized dosage regimens were 117 mg/kg of body weight/day in mice [administered in four fractions 6 h apart (q6h): 61.9%, 18.6%, 11.3%, and 8.21% of total dose] for the bloodstream and 96.7 mg/kg of body weight/day (given q6h as 65.1%, 16.9%, 10.5%, and 7.41%) for the lung model. These validated humanized dosage regimens and population pharmacokinetic models support translational studies with clinically relevant amikacin exposure profiles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".