Short Proline-Rich Lipopeptide Potentiates Minocycline and Rifampin against Multidrug- and Extensively Drug-Resistant Pseudomonas aeruginosa
Bibliographic record
Abstract
ABSTRACT A series of 16 short proline-rich lipopeptides (SPRLPs) were constructed to mimic longer naturally existing proline-rich antimicrobial peptides. Antibacterial assessment revealed that lipopeptides containing hexadecanoic acid (C 16 ) possess optimal antibacterial activity relative to others with shorter lipid components. SPRLPs were further evaluated for their potential to serve as adjuvants in combination with existing antibiotics to enhance antibacterial activity against drug-resistant Pseudomonas aeruginosa . Out of 16 prepared SPRLPs, C 12 -PRP was found to significantly potentiate the antibiotics minocycline and rifampin against multidrug- and extensively drug-resistant (MDR/XDR) P. aeruginosa clinical isolates. This nonhemolytic C 12 -PRP is comprised of the heptapeptide sequence PRPRPRP-NH 2 acylated to dodecanoic acid (C 12 ) at the N terminus. The adjuvant potency of C 12 -PRP was apparent by its ability to reduce the MIC of minocycline and rifampin below their interpretative susceptibility breakpoints against MDR/XDR P. aeruginosa . An attempt to optimize C 12 -PRP through peptidomimetic modification was performed by replacing all l - to d -amino acids. C 12 -PRP demonstrated that it was amenable to optimization, since synergism with minocycline and rifampin were retained. Moreover, C 12 -PRP displayed no cytotoxicity against human liver carcinoma HepG2 and human embryonic kidney HEK-293 cell lines. Thus, the SPRLP C 12 -PRP is a lead adjuvant candidate that warrants further optimization. The discovery of agents that are able to resuscitate the activity of existing antibiotics against drug-resistant Gram-negative pathogens, especially P. aeruginosa , is of great clinical interest.
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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) | 0.000 | 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) | 0.001 | 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".