Optimization of Meropenem Minimum Concentration/MIC Ratio To Suppress In Vitro Resistance of <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
Suppression of resistance in a dense Pseudomonas aeruginosa population has previously been shown with optimized quinolone exposures. However, the relevance to beta-lactams is unknown. We investigated the bactericidal activity of meropenem and its propensity to suppress P. aeruginosa resistance in an in vitro hollow-fiber infection model (HFIM). Two isogenic strains of P. aeruginosa (wild type and an AmpC stably derepressed mutant [MIC = 1 mg/liter]) were used. An HFIM inoculated with approximately 1 x 10(8) CFU/ml of bacteria was subjected to various meropenem exposures. Maintenance doses were given every 8 h to simulate the maximum concentration achieved after a 1-g dose in all regimens, but escalating unbound minimum concentrations (C(min)s) were simulated with different clearances. Serial samples were obtained over 5 days to quantify the meropenem concentrations, the total bacterial population, and subpopulations with reduced susceptibilities to meropenem (>3x the MIC). For both strains, a significant bacterial burden reduction was seen with all regimens at 24 h. Regrowth was apparent after 3 days, with the C(min)/MIC ratio being < or = 1.7 (time above the MIC, 100%). Selective amplification of subpopulations with reduced susceptibilities to meropenem was suppressed with a C(min)/MIC of > or = 6.2 or by adding tobramycin to meropenem (C(min)/MIC = 1.7). Investigations that were longer than 24 h and that used high inocula may be necessary to fully evaluate the relationship between drug exposures and the likelihood of resistance suppression. These results suggest that the C(min)/MIC of meropenem can be optimized to suppress the emergence of non-plasmid-mediated P. aeruginosa resistance. Our in vitro data support the use of an extended duration of meropenem infusion for the treatment of severe nosocomial infections in combination with an aminoglycoside.
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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".