Personalized inhaled bacteriophage therapy for treatment of multidrug-resistant Pseudomonas aeruginosa in cystic fibrosis
Bibliographic record
Abstract
Bacteriophage (phage) therapy, which uses lytic viruses as antimicrobials, is a potential strategy to address the antimicrobial resistance crisis. Cystic fibrosis, a disease complicated by recurrent Pseudomonas aeruginosa pulmonary infections, is an example of the clinical impact of antimicrobial resistance. Here, using a personalized phage therapy strategy that selects phages for a predicted evolutionary trade-off, nine adults with cystic fibrosis (eight women and one man) of median age 32 (range 22–46) years were treated with phages on a compassionate basis because their clinical course was complicated by multidrug-resistant or pan-drug-resistant Pseudomonas that was refractory to prior courses of standard antibiotics. The individuals received a nebulized cocktail or single-phage therapy without adverse events. Five to 18 days after phage therapy, sputum Pseudomonas decreased by a median of 104 CFU ml−1, or a mean difference of 102 CFU ml−1 (P = 0.006, two-way analysis of variance with Dunnett’s multiple-comparisons test), without altering sputum microbiome, and an analysis of sputum Pseudomonas showed evidence of trade-offs that decreased antibiotic resistance or bacterial virulence. In addition, an improvement of 6% (median) and 8% (mean) predicted FEV1 was observed 21–35 days after phage therapy (P = 0.004, Wilcoxon signed-rank t-test), which may reflect the combined effects of decreased bacterial sputum density and phage-driven trade-offs. These results show that a personalized, nebulized phage therapy trade-off strategy may affect clinical and microbiologic endpoints, which must be evaluated in larger clinical trials. A novel personalized phage therapy strategy that selects phages for a predicted evolutionary trade-off may represent a viable alternative approach for the treatment of antimicrobial-resistant bacterial infections.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".