Evaluation of Azithromycin-Bicarbonate against Multidrug-Resistant Pathogens in Topical Murine Models of Infection
Bibliographic record
Abstract
Multidrug-resistant pulmonary infections pose significant therapeutic challenges as treatment options continue to dwindle in the face of rising antimicrobial resistance. Similar challenges arise in the management of wound infections such as those resulting from burn and blast injuries, where resistant pathogens severely limit treatment options. These wounds are further complicated by high microbial loads that exacerbate tissue damage, delay healing, and increase the risk of systemic infection. The escalating threat of antimicrobial resistance highlights the urgent need for innovative therapeutic strategies. This study evaluates the therapeutic potential of a novel topical formulation, azithromycin-bicarbonate (AZM-BIC), for addressing drug-resistant infections in both pulmonary and wound settings. Using murine models of infection in bicarbonate-depleted environments, including lung, blast injury, and burn wound models, topical administration of AZM-BIC enabled the localized delivery of therapeutic concentrations of bicarbonate. In the pulmonary model, AZM-BIC significantly reduced the bacterial burden. In vitro and ex vivo studies revealed AZM-BIC's ability to inhibit biofilm formation, a critical factor in managing chronic infections. In wound infection models, AZM-BIC reduced the bacterial burden and enhanced wound healing. These findings establish AZM-BIC as a promising therapeutic approach, offering a targeted, effective solution for pulmonary infection management and wound care amid the growing threat of antimicrobial resistance. Furthermore, given that azithromycin is a well-established antibiotic and bicarbonate is a physiological component that is safe and well-tolerated, AZM-BIC represents a readily translatable strategy for clinical implementation.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".