Pharmacodynamics of dose-escalated ‘front-loading’ polymyxin B regimens against polymyxin-resistant mcr-1-harbouring Escherichia coli
Bibliographic record
Abstract
Objectives: Gram-negative bacteria harbouring the mcr-1 plasmid are resistant to the 'last-line' polymyxins and have been reported worldwide. Our objective was to define the impact of increasing the initial polymyxin B dose intensity against an mcr-1 -harbouring strain to delineate the impact of plasmid-mediated polymyxin resistance on the dynamics of bacterial killing and resistance. Methods: A hollow fibre infection model (HFIM) was used to simulate polymyxin B regimens against an mcr-1 -harbouring Escherichia coli (MIC 8 mg/L) over 10 days. Four escalating polymyxin B 'front-loading' regimens (3.33, 6.66, 13.3 or 26.6 mg/kg for one dose followed by 1.43 mg/kg every 12 h starting 12 h later) simulating human pharmacokinetics were utilized in the HFIM. A mechanism-based, mathematical model was developed using S-ADAPT to characterize bacterial killing. Results: The 3.33 mg/kg 'front-loading' regimen resulted in regrowth mirroring the growth control. The 6.66, 13.3 and 26.6 mg/kg 'front-loading' regimens resulted in maximal bacterial reductions of 1.91, 3.79 and 6.14 log 10 cfu/mL, respectively. Irrespective of the early polymyxin B exposure (24 h AUC), population analysis profiles showed similar growth of polymyxin B-resistant subpopulations. The HFIM data were well described by the mechanism-based model integrating three subpopulations (susceptible, intermediate and resistant). Compared with the susceptible subpopulation of mcr-1 -harbouring E. coli , the resistant subpopulation had an approximately 10-fold lower rate of killing due to polymyxin B treatment. Conclusions: Manipulating initial dose intensity of polymyxin B was not able to overcome plasmid-mediated resistance due to mcr-1 in E. coli . This reinforces the need to develop new combinatorial strategies to combat these highly resistant Gram-negative bacteria.
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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) | 0.000 | 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 | 0.001 |
| 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".