Routine Doses of Enrofloxacin Achieve Pharmacodynamic Target Likely to Treat Susceptible and Susceptible Dose‐Dependent Bacterial Infections in Critically Ill Dogs
Bibliographic record
Abstract
The fluoroquinolones, particularly enrofloxacin, are frequently used to treat life-threatening bacterial infections in small animal emergency and critical care practice. Achieving therapeutic plasma concentrations is essential for effective treatment, whereas inadequate concentrations select for resistance among Enterobacterales and Pseudomonas aeruginosa . We conducted a prospective observational study in 19 critically ill dogs to evaluate the pharmacokinetics (PK) of enrofloxacin and its active metabolite ciprofloxacin after administration of a standardized dosage (10 mg/kg IV q24 h). Plasma concentrations were measured at multiple time points using liquid chromatography-mass spectrometry, and PK modeling was performed to determine best-fit compartmental models. A 2-compartment model best described enrofloxacin PK. There was considerable between-dog variation in PK parameters, likely due to known challenges of drug dosing in critical illness. The percentage conversion of enrofloxacin to ciprofloxacin was lower than has previously been reported in healthy dogs. Pharmacodynamic analyses suggest that enrofloxacin administered at 10 mg/kg IV q24 h to critically ill dogs will likely result in effective treatment of infections by susceptible and susceptible dose-dependent bacteria, and achieved concentrations may be sufficient to reduce the risk of AMR development.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".