Applications of Polymeric and Lipid Nanoparticles in Ophthalmic Pharmaceutical Formulations: Present and Future Considerations
Bibliographic record
Abstract
The unique properties and characteristics of ocular tissues and the whole set of defence mechanisms of the ocular globe make the instillation of ocular drugs into a difficult task with a low rate of therapeutic response. One of the challenges for the new generation of ophthalmic pharmaceutical formulations is to increase the bioavailability of drugs administered by the ocular route and, therefore, their therapeutic efficacy. This can be achieved with the use of some strategies that provide an increase in the formulation pre-corneal residence time, mucoadhesion and penetration across the eye tissues. Colloidal carrier systems have been very successfully used for the selective and targeted delivery of drugs for several routes of administration. In this context, nanoparticles prepared with specific polymers or lipids and coated, dispersed or suspended in polymer solutions with mucoadhesion properties or in situ gelling properties will be an excellent strategy that deserves attention and further research. In this review, the characteristics and main properties of polymeric and lipid nanoparticles are discussed and examples and advantages of the application of these colloidal carrier systems for the ophthalmic administration of drugs are presented. The future directions of the research required in this specific field are also presented.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".