Validated Preclinical Murine Model for Therapeutic Testing against Multidrug Resistant <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
Abstract The rise in infections caused by antibiotic resistant bacteria is outpacing the development of new antibiotics. The ESKAPE pathogens ( Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa , and Enterobacter species) are a group of clinically important bacteria that have developed resistance to multiple antibiotics and are commonly referred to as multidrug resistant (MDR). The medical and research communities have recognized that without new antimicrobials, infections by MDR bacteria will soon become a leading cause of morbidity and mortality. Therefore, there is an ever growing need to expedite the development of novel antimicrobials to combat these infections. Toward this end, we set out to refine an existing murine model of pulmonary Pseudomonas aeruginosa infection to generate a robust preclinical tool that can be used to rapidly and accurately predict novel antimicrobial efficacy. This refinement was achieved by characterizing the virulence of a panel of genetically diverse MDR P. aeruginosa strains in this model, both by LD 50 analysis and natural history studies. Further, we defined two antibiotic regimens (aztreonam and amikacin) that can be used a comparators during the future evaluation of novel antimicrobials, and validated that the model can effectively differentiate between successful and unsuccessful treatment as predicted by in vitro inhibitory data. This validated model represents an important tool in our arsenal to develop new therapies to combat MDR P. aeruginosa , with the ability to provide rapid preclinical evaluation of novel antimicrobials that can also serve to support data from clinical studies during the investigational drug development process.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".