Comparative Pharmacodynamics of Ceftobiprole, Daptomycin, Linezolid, Telavancin, Tigecycline, and Vancomycin in the Treatment of Methicillin Resistant <em>Staphylococcus aureus</em>: A Monte Carlo Simulation Analysis
Bibliographic record
Abstract
Background/Objectives: Appropriate initial treatment choices for methicillin resistant Staphylococcus aureus (MRSA) infections are very critical. The aim of this study was to compare the ability of Ceftobiprole, Daptomycin, Linezolid, Telavancin, Tigecycline, and Vancomycin to achieve their requisite pharmacokinetic/pharmacodynamic (PK/PD) target against clinical MRSA isolates.\nMethods: Monte Carlo Simulations were performed to simulate the PK/PD indices of the investigated antimicrobials. Population Pharmacokinetic data and Pharmacodynamic indices were integrated into Monte Carlo Simulation routine with 10,000 iterations. Probability of target attainment (PTA) was estimated at MIC values ranging from 0.03-32 μg/ml to define the PK/PD susceptibility breakpoints. Cumulative fraction of response (CFR) was computed using MIC data from the Canadian National Ward (CAN-Ward) study collected in 2007, 2008 and 2009.\nResults: Analysis of the simulation results suggested the breakpoints of 8μg/ml for Ceftobiprole, 0.12 μg/ml for Daptomycin and Tigecycline, 0.5 μg/ml for Telavancin and 1 μg/ml for Linezolid and Vancomycin. The estimated CFR were 100, 66.5, 84, 89.1, 98.2, 60, 97.5 % for Ceft obiprole, Daptomycin (4mg/kg/day), Daptomycin (6mg/kg/day), Linezolid, Telavancin, Tigecycline, Vancomycin (2gm/day) and Vancomycin (3gm/day), respectively.\nConclusions: Ceftobiprole and Telavancin have the highest probability of achieving favorable outcome against MRSA infections. The susceptibility results suggested a further reduction of the vancomycin breakpoint to 1 μg/ml.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".