Mutant Prevention Concentrations of Doripenem and Meropenem Alone and in Combination with Colistin (Polymyxin E), Levofloxacin and Tobramycin in <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
BACKGROUND: With a limited number of available antimicrobial agents to treat Pseudomonas aeruginosa infections, the prevention of emergence of antimicrobial resistance and its subsequent spread is critical. In the present study, the mutant prevention concentration (MPC) of doripenem was examined and compared with meropenem for its ability to prevent resistant mutant selection for P aeruginosa when used alone and in combination with the other antipseudomonal agents colistin (polymyxin E), levofloxacin and tobramycin. OBJECTIVE: To determine if two antimicrobial agents that possessed different mechanisms of action and separate demonstrated activities against P aeruginosa would produce a reduced MPC in combination compared with the MPC of each agent alone. METHODS: Twelve clinical isolates of P aeruginosa were plated on Mueller‐Hinton agar containing 1×, 2×, 4×, 8×, 16× and 32× the doripenem, imipenem or meropenem minimum inhibitory concentration (MIC), and also on agar containing doripenem or meropenem in combination with either tobramycin (6 μg/mL), colistin (2 μg/mL or 8 μg/mL), levofloxacin (8 μg/mL) or azithromycin (0.4 μg/mL). The MPC for each antimicrobial agent‐isolate combination was defined as the lowest antibiotic concentration that prevented the visible growth of mutant colonies at 48 h of incubation. The MPC/MIC (μg/mL) ratio was defined as the ratio of the MPC obtained to the original MIC. RESULTS: The MPC/MIC ratios of doripenem alone ranged from 8 to 32 for the twelve isolates tested compared with 32 for two isolates and greater than 32 for 10 isolates with imipenem, and 32 for three isolates and greater than 32 for nine isolates with meropenem. All antimicrobials tested exhibited markedly elevated MPCs compared with their original MICs with MPC/MIC ratios of 8 to 32 for doripenem, 32 to greater than 32 for imipenem, 32 to greater than 32 for meropenem, 32 to greater than 32 for colistin (tested at 2 μg/mL), 8 to 16 for levofloxacin and 8 to 32 for tobramycin. When a second antimicrobial was used in combination with doripenem, the MPC/MIC ratio decreased up to twofold for doripenem combined with colistin (2 μg/mL), decreased four‐ to 16‐fold for doripenem combined with colistin (8 μg/mL), decreased eight‐ to 32‐fold for doripenem combined with levofloxacin, and decreased four‐ to 16‐fold for doripenem combined with tobramycin. Adding a second antimicrobial in combination with meropenem resulted in the following decreases in MPC/MIC: no decrease for meropenem combined with colistin (2 μg/mL), four to greater than eightfold decrease for meropenem combined with colistin (8 μg/mL), four‐ to 16‐fold decrease for meropenem combined with levofloxacin, and two‐ to 16‐fold decrease for meropenem combined with tobramycin. For all antimicrobial combinations tested, doripenem yielded greater decreases in MPC/MIC ratios than did meropenem. CONCLUSION: The present study found that individual antipseudomonal antimicrobial agents tested against 12 clinical isolates of P aeruginosa had eight‐ to greater than 32‐fold higher MPCs than MICs, that combining doripenem or meropenem with a second active antipseudomonal agent with a different mechanism of action was more effective at preventing resistance selection than the two agents used individually, and finally, that doripenem was less likely than both imipenem and meropenem to select for spontaneous resistance mutants of P aeruginosa.
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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 | 0.001 |
| 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 | 0.001 |
| 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".