A Population Pharmacokinetic–Metabolism Model for Individualizing Ciprofloxacin Therapy in Ophthalmology
Bibliographic record
Abstract
The purpose of this study was to construct a population pharmacokinetic (PK) metabolism (MB) model to describe ciprofloxacin (C) concentrations in plasma and vitreous and aqueous humors in 26 patients. Ciprofloxacin was given as a 3-day oral prophylactic treatment to 26 patients before vitrectomy. Plasma, vitreous, and aqueous humor samples were collected from patients at different times on the day of surgery. Patients were phenotyped for CYP 1A2 activity using caffeine. Ciprofloxacin and caffeine concentrations were determined using validated HPLC assays. All concentrations of ciprofloxacin were simultaneously modeled using a four-compartment PK-MB model. Creatinine clearance and CYP 1A2 activity were modeled as surrogate markers of renal and hepatic clearances, respectively. Population PK was performed with IT2S, and simulations were performed with ADAPT-II. No eye infections were observed in any of the patients enrolled in the study, and there were only minimal effects on vitreous and aqueous concentrations after ocular drops were added to the oral treatments. The model that best described the concentrations of ciprofloxacin in serum and in aqueous and vitreous humor was a four-compartment PK linear model. Simulated AUCs of ciprofloxacin mean concentrations in the aqueous and vitreous humors were 17 +/- 9 and 10 +/- 8% of the systemic AUC, respectively. The terminal elimination half-life of the compound was (mean +/- SD) 5.0 +/- 2.8 hours. The apparent volume of distribution (Vss/F) was calculated to be 122.1 +/- 39.7 L. This PK-MB model may be very useful in optimizing treatments of various eye infections with ciprofloxacin. The results of this study suggest that giving ciprofloxacin orally for 2 days preceding surgery may prevent endophthalmitis from occurring, consequently abrogating the need for administering antibiotics via intraocular injections.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".