Topical Antiangiogenic SRPK1 Inhibitors Reduce Choroidal Neovascularization in Rodent Models of Exudative AMD
Bibliographic record
Abstract
PURPOSE: Exudative AMD (wet AMD) is treated by monthly injection into the eye of anti-VEGF proteins. VEGF is alternatively spliced to produce numerous isoforms that differ in angiogenic activity. Serine-rich protein kinase-1 (SRPK1) has been identified as a regulator of pro-angiogenic VEGF splicing by phosphorylating serine-rich splicing factor-1 (SRSF1), which binds to VEGF pre-mRNA. We tested the hypothesis that topical (eye drop) SRPK1-selective inhibitors could be generated that reduce pro-angiogenic isoforms, and prevent choroidal neovascularization in vivo. METHODS: Novel inhibitors were tested for SRPK inhibition in vitro, pro-angiogenic VEGF production in RPE cells by PCR and ELISA, and for inhibition of choroidal neovascularisation in mice and rats. RESULTS: A novel disubstituted furan inhibitor was selective for the SRPK family of kinases and reduced expression of pro-angiogenic but not antiangiogenic VEGF isoforms. This inhibitor and previously identified SRPK inhibitors significantly reduced choroidal neovascularisation in vivo. Topical administration of SRPK inhibitors dose-dependently blocked CNV with an EC50 of 9 μM. CONCLUSIONS: These results indicate that novel SRPK1 selective inhibitors could be a potentially novel topical (eye drop) therapeutic for wet AMD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".