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 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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".