NOVEL APPROACHES FOR PREVENTION/ERADICATION OF PSEUDOMONAS AERUGINOSA LUNG INFECTIONS IN MURINE MODELS
Bibliographic record
Abstract
Pseudomonas aeruginosa is one of the top three causes of opportunistic human infections in different categories of patients. Antibiotics are used as first line drugs for the treatment of these infections. However, frequently observed inefficacy of these treatments is linked to the high levels of intrinsic and acquired resistance of P. aeruginosa to these agents. Unfortunately during the last decades few novel anti-pseudomonas drugs have arrived to clinical phases. Moreover, in the past, several vaccine candidates against P. aeruginosa have been tested in pre-clinical trials, few have reached clinical phases but none of these has obtained market authorization. Thus, this scenario highlights the need of new therapeutic options for the prevention/eradication of P. aeruginosa. In the first part of this work the efficacy of a novel Protein Epitope Mimetic antibiotic named New Chemical Entity (NCE) was assessed in murine models of P. aeruginosa acute and chronic airways infection, including CFTR-deficient mice. A comparison between different administration routes (systemic versus pulmonary) was also considered. NCE demonstrated a remarkably efficacy in reducing the bacterial load and the inflammation in the lung when administered locally in all murine models tested. The efficacy of this novel antibiotic peptide was superior to ciprofloxacin, one of the current available treatments. Moreover pharmacokinetic studies confirmed that NCE reached favorable concentrations in the lung after pulmonary administration. These results supported the development of NCE as a potential novel inhaled therapy to treat P. aeruginosa airways infections. In the second part of this work, novel vaccine candidates against P. aeruginosa, identified by the combination of advanced whole genomic approaches including the ?reverse vaccinology?, were tested in murine models of acute pneumonia. 32 vaccine candidates were tested for their ability to protect against a lethal dose of P. aeruginosa. 10 proteins showed an increase in the survival curves when compared with a negative control group. Further characterization suggested that the vaccine candidates were immunogenic, expressed in bacterial culture and surface exposed. When combinations of two proteins were tested in murine models, five of them showed a statistically significant increase in both the survival curves and mean survival time compared with a negative control group. The highest protection rate (50%) was achieved by the combination of two unknown proteins. Overall these results suggested that the combination of comparative genome analysis and innovative methods in vaccine design are valid tools for the identification of novel vaccine candidates against 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".