Antibiotic entrapment in antibacterial micelles as a novel strategy for the delivery of challenging antibiotics from silica nanoparticles
Bibliographic record
Abstract
Silica materials are popular in biomedical applications as composites and drug delivery platforms due to their low toxicity and biocompatibility. Mesoporous silica nanoparticles are attractive drug delivery systems based on their porous silica framework with high surface area. In the preparation of mesoporous silica frameworks, most commonly, MCM-41, the efficient removal of the template responsible for introducing porous networks, cetyltrimethyl ammonium bromide (CTAB), is a critical step due to the template's high toxicity in the environment and human health. In this work, we present a new one-pot approach of introducing challenging antibiotics within the silica framework without the need of toxic templates, but instead using micelle formation by an antibacterial agent. We demonstrate that micelles formed by cetylpyridinium chloride (CPC), a known antibacterial agent, entrap antibiotics such as rifampicin and ciprofloxacin. Extensive NMR studies elucidate the precise localisation of the antibiotic within the CPC micelle. Ciprofloxacin is placed between the outer and palisade region while rifampicin is located further into the hydrophobic CPC micelle core. In both cases, the formation of the silica framework can be built around the CPC-antibiotic loaded micelles. The resulting silica nanoparticles show loading of both CPC and antibiotic agents, porosity and dual antibacterial release upon disruption of the micelle within the silica framework. The design not only provides a strategy of a therapeutic design to form porous frameworks but also highlights the potential of precise antibiotic dose and release in nanoparticle systems.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".