Design of liposomal nanocarriers with a potential for combined dexamethasone and bevacizumab delivery to the eye
Bibliographic record
Abstract
Clinicians face numerous challenges when delivering medications to the eyes topically because of physiological barriers, that can inhibit the complete dose from getting to the intended location. Due to their small size, the ability to deliver drugs of different polarities simultaneously, and their biocompatibility, liposomes hold great promise for ocular drug delivery. This study aimed to develop and characterise a dual loaded liposome formulation encapsulating Bevacizumab (BEV) and Dexamethasone (DEX) that possessed the physicochemical attributes suitable for topical ocular delivery. Liposomes were prepared by using thin film hydration followed by extrusion, and the formulations were optimised using a design of experiments approach. Physicochemical characterisation along with cytocompatibility and bioactivity of the formulations were assessed. Liposomes were successfully prepared with a particle size of 139 ± 2 nm, PDI 0.03 ± 0.01 and zeta potential -2 ± 0.7 mV for the optimised formulation. BEV and DEX were successfully encapsulated into the liposomes with an encapsulation efficiency of 97 ± 0.5 % and 26 ± 0.5 %, respectively. A sustained release of BEV was observed from the liposomes and the bioactivity of the formulation was confirmed using a wound healing assay. In summary, a potential topical eye drop drug delivery system, which can co-load DEX and BEV was developed and characterised for its potential to be used in ocular drug delivery.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".