DRUG DELIVERY SOLUTIONS FOR THE POTENTIAL TREATMENT OF CHILDHOOD OCULAR CONDITIONS
Bibliographic record
Abstract
Ocular drug delivery remains a challenge due to the anatomical and physiological barriers of the eye. Topical drops have poor penetration and side effects, and injections are highly invasive; an alternative approach is transscleral delivery. Sustained drug release systems utilizing this delivery pathway are non-invasive yet provide extended treatment periods with single doses. Specifically, such systems would be beneficial for treating pediatric populations for diseases such as retinoblastoma and myopia. A chemo-preventative treatment for retinoblastoma, a childhood ocular cancer, has been proposed utilizing CdK inhibitors such as roscovitine and R547. Myopia, a more common, chronic condition in children, can be treated with atropine, though the dosing must be tightly controlled. Three novel delivery systems have been developed in this work to deliver these drugs, generating potential treatment platforms for these conditions. Contact lenses have been investigated previously to deliver various therapeutics, as they act as a slow, equilibrating reservoir, and the prolonged corneal residence time improves bioavailability. Silicone lenses, with their ability for continuous wear, have been explored looking at various material compositions and drug loading levels and techniques to confirm the feasibility of this system for these conditions (Chapter 2). This work has been extended to include covalent tethers for roscovitine, ensuring that it is able to survive a more commercially-relevant synthesis and providing additional ways to tailor the drug release profiles (Chapter 3). Alternatively, amphiphiles, which can self-assemble in the presence of excess water, have been explored for their drug delivery potential. Novel materials consisting of oleoylethanolamide and linoleoylethanolamide were developed to exhibit cubic phase transitions at physiologically relevant conditions, and their subsequent drug release characteristics were explored (Chapter 4). Silicone-based materials have broad usages as biomaterials, and a novel approach of incorporating PEG into the material without the use of metal catalysts were explored (Chapter 5). These exhibited a significant reduction in protein fouling, and material compositions were further iterated on, and their subsequent drug delivery capabilities were examined (Chapter 6). Overall, these three approaches have been able to successfully delivery roscovitine, R547 and atropine in non-invasive manners that have the potential to be an appropriate treatment for the childhood conditions they are intended to treat.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.117 | 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".