Next-generation calcineurin inhibitors for ophthalmic indications
Bibliographic record
Abstract
Calcineurin inhibitors (CNIs) are potent immunosuppressants that reversibly inhibit T-cell proliferation and prevent the release of pro-inflammatory cytokines by blocking the activity of calcineurin, a ubiquitous enzyme that is found in cell cytoplasm. CNIs can be highly effective in immune-mediated ophthalmic diseases such as uveitis, dry eye syndrome and inflammatory blepharitis, as well as for the prevention of rejection in corneal transplants. ISA-247/LX-211 is a novel CNI that is in Phase III clinical development for the treatment of various forms of non-infectious uveitis. ISA-247/LX-211 is a rationally designed analog of ciclosporin A that exhibits more predictable pharmacokinetic and pharmacodynamic properties and a 4-fold greater calcineurin inhibition than its parent compound, ciclosporin A. ISA-247/LX-211 has been observed to be effective, well-tolerated, and safe in early clinical trials, exhibiting a much wider therapeutic window compared with classic CNIs, such as ciclosporin A and tacrolimus. An alternative approach to widening the therapeutic window for the therapy of ophthalmic disorders lies in local delivery of CNIs through polymeric implants that release the drug over long periods of time. The silicone matrix episcleral implant LX-201 is in Phase III development at present for the prevention of rejection in high-risk cornea transplantation.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".