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Record W2180702109 · doi:10.18433/j3h60p

Improvements in Topical Ocular Drug Delivery Systems: Hydrogels and Contact Lenses

2015· review· en· W2180702109 on OpenAlexvenueno aff
Andreza Maria Ribeiro, Ana Figueiras, Francisco Veiga

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2015
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvanced Drug Delivery Systems
Canadian institutionsnot available
Fundersnot available
KeywordsDrug deliveryMedicineDrugCorneaSelf-healing hydrogelsNanotechnologyOphthalmologyPharmacologyMaterials science

Abstract

fetched live from OpenAlex

PURPOSE: Conventional ophthalmic systems present very low corneal systemic bioavailability due to the nasolacrimal drainage and the difficulty to deliver the drug in the posterior segment of ocular tissue. For these reasons, recent advances have focused on the development of new ophthalmic drug delivery systems. This review provides an insight into the various constraints associated with ocular drug delivery, summarizes recent findings in soft contact lenses (SCL) and the applications of novel pharmaceutical systems for ocular drug delivery. Among the new therapeutic approaches in ophthalmology, SCL are novel continuous-delivery systems, providing high and sustained levels of drugs to the cornea. The tendency of research in ophthalmic drug delivery systems development are directed towards a combination of several technologies (bio-inspired and molecular imprinting techniques) and materials (cyclodextrins, surfactants, specific monomers). There is a tendency to develop systems which not only prolong the contact time of the vehicle at the ocular surface, but also at the same time slow down the clearance of the drug. Different materials can be applied during the development of contact lenses and can be combined with natural inspired strategies of drug immobilization and release, providing successful tools for ocular drug delivery systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.270
GPT teacher head0.521
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations36
Published2015
Admission routes1
Has abstractyes

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