Carboxymethylated Phytoglycogen Nanocarriers for the Sustained Biphasic Release of the Broad-Spectrum Antibiotic Amikacin
Bibliographic record
Abstract
Bacterial infections pose a global health threat, not only because they cause serious complications, but also due to the alarming rise in antibiotic resistance that renders many treatments ineffective. Strategies of antibiotic delivery are being explored to combat these infections, especially through inhalation and topical application, which could provide a highly localized treatment with minimal systemic toxicity. Amikacin (AMK), an aminoglycoside antibiotic, exhibits outstanding antibacterial activity; however, its use is limited by low bioavailability and high toxicity, challenges that can be addressed with a protective nanocarrier. Phytoglycogen nanoparticles are biodegradable, nontoxic, and generally recognized as safe (GRAS). We report here the carboxymethylation of such particles for the local delivery of AMK, wherein electrostatic association was employed for the loading of AMK into the nanocarriers. The nanoformulation was evaluated for its colloidal stability, loading capacity, and AMK release characteristics, as well as its antibacterial activity and cytotoxicity. The carboxymethylated nanophytoglycogen shows a very high loading capacity for AMK. The AMK-loaded nanophytoglycogen not only maintains the AMK antibacterial activity but also exhibits a biphasic release profile for AMK, combining the benefits of immediate and sustained release. This allows the required local drug concentration to be quickly reached and sustained above the MIC for longer periods, reducing the dosing frequency. The formulation exhibits low cytotoxicity toward human cells, highlighting its broad potential applications. Additionally, it exhibits no harmful effects on a lung surfactant model membrane, supporting the potential use of chemically functionalized nanophytoglycogen as nanocarriers for inhalation delivery of antibiotics to treat respiratory 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".