Delivery of a novel neuroprotective compound to the retina in rat and rabbit animal models
Bibliographic record
Abstract
Posterior segment-related diseases are among the leading causes of irreversible blindness and loss of vision globally. These diseases are extremely difficult to treat due to the drug delivery barriers posed by the eye, among other challenges. One delivery method that bypasses many of these obstacles, albeit not without risk, is ocular injections, and long-acting formulations such as implants can improve patient compliance by allowing for longer intervals between injections. Here, we report our development of a preclinical in situ-forming implant dosage form that provides sustained release of a novel compound, DKR-1677, with a target in the retina. An in situ-forming implant based on polylactic co glycolic acid (PLGA) was chosen in this preclinical stage because it is readily translatable to a preformed implant product. The formulations were tested in vitro, in rat and rabbit animal models for drug release and pharmacokinetics. A two-step in vitro dissolution method with implant formation in a biorelevant gel followed by incubation in release media showed a 30-day three-phase release profile with an initial burst release of 36.04 ± 4.23 %, a plateau, and a controlled release up to 93.75 ± 4.68 % at day 30, typical of PLGA-based implant formulations. Immediate and controlled-release formulations were tested in rat and rabbit animal models and confirmed that DKR-1677 is taken up by the retina after intravitreal administration. Furthermore, the in situ-forming implant was found to prolong drug presence in the retina to 30 days following a single administration, confirming that a PLGA-based implant is a viable approach for this drug candidate.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".