Acetic and Acrylic Acid Molecular Imprinted Model Silicone Hydrogel Materials for Ciprofloxacin-HCl Delivery
Bibliographic record
Abstract
Contact lenses, as an alternative drug delivery vehicle for the eye compared to eye drops, are desirable due to potential advantages in dosing regimen, bioavailability and patient tolerance/compliance. The challenge has been to engineer and develop these materials to sustain drug delivery to the eye for a long period of time. In this study, model silicone hydrogel materials were created using a molecular imprinting strategy to deliver the antibiotic ciprofloxacin. Acetic and acrylic acid were used as the functional monomers, to interact with the ciprofloxacin template to efficiently create recognition cavities within the final polymerized material. Synthesized materials were loaded with 9.06 mM, 0.10 mM and 0.025 mM solutions of ciprofloxacin, and the release of ciprofloxacin into an artificial tear solution was monitored over time. The materials were shown to release for periods varying from 3 to 14 days, dependent on the loading solution, functional monomer concentration and functional monomer:template ratio, with materials with greater monomer:template ratio (8:1 and 16:1 imprinted) tending to release for longer periods of time. Materials with a lower monomer:template ratio (4:1 imprinted) tended to release comparatively greater amounts of ciprofloxacin into solution, but the release was somewhat shorter. The total amount of drug released from the imprinted materials was sufficient to reach levels relevant to inhibit the growth of common ocular isolates of bacteria. This work is one of the first to demonstrate the feasibility of molecular imprinting in model silicone hydrogel-type materials.
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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 | 0.000 | 0.000 |
| 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".