Resurrecting the Dead: Mitigating Efflux-Pump Inhibitor Toxicity using a Liposomal Delivery System to Recover Efficacy of Antimicrobial Drugs
Bibliographic record
Abstract
Introduction: Antimicrobial resistance (AMR) has become concerningly prevalent on a global scale as many infectious agents have evolved to evade antimicrobials effects, making it difficult to treat infectious diseases. Pseudomonas aeruginosa is a multiresistant bacteria that requires urgent attention as it is detrimental in lung infections, particularly in individuals with cystic fibrosis. Activity of membrane-embedded efflux pumps, such as the MexAB-OprM pump, is a principal mechanism by which bacterial species become resistant to antimicrobials. Efflux pump inhibitors (EPIs) have recently emerged as a strategy to prevent the expulsion of administered antimicrobials, thereby resensitizing resistant bacteria to antibiotics. Phenylalanine‐arginine β‐naphthylamide (PAβN) is an EPI that inhibits a number of different pumps, including the MexAB‐OprM efflux system. Despite EPIs providing a partial solution to AMR, they have been shown to be toxic to humans, which has impeded their entry into clinical application. We propose that by inserting the PAβN into a liposomal delivery system, the cytotoxic effects against human cells will be lowered without decreasing the EPI’s inhibitory activity. Methods: To test this, resistant P. aeruginosa strains will be administered with liposomal-encased PAβN and ampicillin to measure efflux activity and inhibited growth, whereas human pulmonary epithelial cells will be exposed to liposomal-encased PAβN to study cell viability. Results: Liposomal EPI are expected to maintain inhibitory activity and resistant bacteria would become re-susceptible to antibiotics when treated with the liposomal EPI. Discussion: By analyzing efflux rate to measure the liposomal EPI’s activity, its activity level should be comparable to free EPI. The resensitization assay would be interpreted to show that the bacteria are susceptible to antibiotics again. Conclusion: If effective, EPIs may become a potential therapeutic to combat AMR in infections by reviving the use of antimicrobials that have become ineffective. Restoring the activity of already approved antibiotics through potential co-administration with liposome-encapsulated EPIs will be a cost-effective and worthwhile approach to combat AMR.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".