A Macrocyclic Calpain Inhibitor Slows the Development of Inherited Cortical Cataracts in a Sheep Model
Bibliographic record
Abstract
PURPOSE: We used sheep with an autosomal dominant gene for cortical cataract as an animal model to evaluate novel macrocyclic calpain inhibitors with potential for the medical treatment of human cataract. METHODS: The macrocyclic aldehyde, CAT811, identified previously as a calpain inhibitor that prevents calcium-induced opacification in cultured sheep lenses, was tested for its ability to protect cytoskeletal proteins from calpain proteolysis. CAT811 and its alcohol analogue, CAT505, were formulated separately into ointments, and each was applied twice daily to the right eye of sheep with early cataracts for five months. Progress of cataracts in the sheep was determined by ophthalmologic examination and comparison with a matched sample of sheep treated similarly with ointment that did not contain the active ingredient. RESULTS: The novel macrocyclic aldehyde, CAT811, was able to inhibit calpain proteolysis of lens cytoskeletal proteins at micromolar concentrations. When applied topically to the eyes of sheep, CAT811 was able to slow cataract development by 27% in the initial three months of treatment (P < 0.05). Its alcohol analogue, CAT505, was not able to slow cataractogenesis significantly. CONCLUSIONS: The inherited sheep cataract provides a reproducible model of cortical cataract over a time scale of several months. The data reported here, using this model, demonstrated the potential of the macrocyclic calpain inhibitor, CAT811, to act as a therapeutic for treatment of cortical cataract.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".