Altering the release of tobramycin by incorporating poly(ethylene glycol) into model silicone hydrogel contact lens materials
Bibliographic record
Abstract
Delivery of drugs from contact lens materials is attractive for a number of reasons. However, the controlled delivery of hydrophilic drugs can be difficult to achieve due to the burst release of drug that is associated with materials of high water content, such as hydrogels. Silicone hydrogels have significant potential for drug delivery due to their increased hydrophobicity and the tortuous nature of the pores, overcoming some of the limitations associated with conventional hydrogel materials. The aim of this study was to examine the potential of model poly(ethylene glycol) (PEG) containing silicone hydrogels for delivery of hydrophilic aminoglycoside antibiotics. It was hypothesized that PEG, a polymer that has seen extensive use in biomedical applications, will provide in addition to hydrophilicity and protein repulsion, a mechanism for controlling the delivery of this hydrophilic antibiotic. PEG was combined with the macromer TRIS to create the model silicone hydrogel materials. The optical and physical properties of the novel TRIS-co-PEG silicone hydrogels exhibited excellent transparency, appropriate refractive index and high transmittance indicating minimal phase separation. Desirable properties such as wettability and protein repulsion were maintained across a wide range of formulations. The water content was found to be highly correlated with the ethylene oxide content. Drug release could be influenced through PEG content and was found to fit Higuchi-like kinetics. Overall, the study demonstrates that incorporation of PEG into a model silicone hydrogel could be used to control the release of a hydrophilic compound. Data suggests this is related to the unique structure and properties of PEG, which alter the types of water found in each formulation and the water content.
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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) | 0.000 | 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".