Monte Carlo simulation evaluation of tigecycline dosing for bacteria with raised minimum inhibitory concentrations in non-critically ill adults
Bibliographic record
Abstract
Tigecycline is one of few antibiotics active against multidrug-resistant bacteria; however, the assessment of dosing strategies to optimize its activity is needed. The purpose was to use Monte Carlo Simulation (MCS) to determine if safe tigecycline dosing options attaining breakpoints for pharmacokinetic/pharmacodynamic (PK-PD) targets in non-critically ill adults could be identified. Publications that evaluated tigecycline dosing regimens and provided mean PK variables of interest (minimum 2 of: elimination rate constant or half-life and volume of distribution or clearance), with SDs, were included. Weighted mean (±SDs) for each PK parameter were determined. Food and Drug Administration minimum inhibitory concentration (MIC) tigecycline breakpoints for susceptible (MIC ≤ 2 μg/mL), intermediate (MIC 4 μg/mL), and resistant (MIC ≥ 8 μg/mL) Enterobacteriaceae were used. MCS probability distributions for PK-PD target attainment of AUC for total tigecycline plasma concentration from 0 to 24 h following an intravenous dose (AUC total, 0-24h ) to MIC ratios of ≥ 18, 7, and 4.5 were generated, with success defined as ≥ 80% probability of target attainment at a given MIC. Ten studies ( n = 442) were eligible. Tigecycline 150 mg IV q12h for ward patients with resistant bacteria up to a MIC of 0.48, 1, and 2 μg/mL for an AUC total, 0-24h /MIC target attainment of 18, 7, and 4.5, respectively, may be appropriate. Bacterial infections with tigecycline MICs ≥ 0.48–2 μg/mL, depending on AUC total, 0-24h /MIC target, may require treatment with alternate antibiotics due to target attainment failure.
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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 | 0.003 | 0.002 |
| 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 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".