The Next Generation of Molecular and Cellular Therapeutics for Inherited Retinal Disease
Bibliographic record
Abstract
Inherited retinal degenerations (IRDs) are a diverse group of conditions that are often characterized by the loss of photoreceptors and blindness. Recent innovations in molecular biology and genomics have allowed us to identify the causative defects behind these dystrophies and to design therapeutics that target specific mechanisms of retinal disease. Recently, the FDA approved the first in vivo gene therapy for one of these hereditary blinding conditions. Current clinical trials are exploring new therapies that could provide treatment for a growing number of retinal dystrophies. While the field has had early success with gene augmentation strategies for treating retinal disease based on loss-of-function mutations, many novel approaches hold the promise of offering therapies that span the full spectrum of causative mutations and mechanisms. Here, we provide a comprehensive review of the approaches currently in development including a discussion of retinal neuroprotection, gene therapies (gene augmentation, gene editing, RNA modification, optogenetics), and regenerative stem or precursor cell-based therapies. Our review focuses on technologies that are being developed for clinical translation or are in active clinical trials and discusses the advantages and limitations for each approach.
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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 | 0.001 |
| 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) | 0.005 | 0.003 |
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".