Nonclinical Development of ENV905 (Difluprednate) Ophthalmic Implant for the Treatment of Inflammation and Pain Associated with Ocular Surgery
Bibliographic record
Abstract
PURPOSE: Topical corticosteroids are widely used in the treatment of inflammation and pain after ocular surgery, but they possess several shortcomings, including frequent dosing and low patient adherence. We evaluated the efficacy and pharmacokinetics of ENV905 (difluprednate or DFBA) Ophthalmic Implant, a single-dose drug delivery system, compared with 0.05% Durezol. METHODS: technology was used to fabricate ENV905 implants for either intracameral (IC) or subconjunctival (SCJ) delivery of extended-release DFBA. A postoperative inflammation model and ocular pharmacokinetics studies of ENV905 or Durezol were conducted in albino rabbits for a maximum of 12 weeks. RESULTS: Suppression of ocular inflammation was marked for both IC and SJC ENV905 compared with placebo, and it was superior or equivalent to that observed with QID Durezol. Concentrations of desacetyl difluprednate (DFB, active metabolite) peaked on day 1 and tapered over time for ENV905, with IC ENV905 delivering DFB to the target tissue at the time of greatest inflammation, whereas SJC produced a longer duration of exposure. Durezol eyes demonstrated consistent exposure over time with maximal exposure in the cornea. Although the pharmacokinetic profile differed for the two routes, efficacy was similar. CONCLUSION: ENV905 was well tolerated and demonstrated a robust reduction in ocular inflammation with targeted drug delivery. The results from these studies show that ENV905 provides a sustained therapeutic effect after a single dose. By resolving low patient compliance and eliminating the peaks and troughs in drug concentration, sustained drug delivery via ENV905 may further improve the overall control of postoperative inflammation and pain.
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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) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".