Genetic treatment for autosomal dominant inherited retinal dystrophies: approaches, challenges and targeted genotypes
Bibliographic record
Abstract
Inherited retinal diseases (IRDs) have been in the front line of gene therapy development for the last decade, providing a useful platform to test novel therapeutic approaches. More than 40 clinical trials have been completed or are ongoing, tackling autosomal recessive and X-linked conditions, mostly through adeno-associated viral vector delivery of a normal copy of the disease-causing gene. However, only recently has autosomal dominant (ad) disease been targeted, with the commencement of a trial for rhodopsin (RHO)-associated retinitis pigmentosa (RP), implementing antisense oligonucleotide (AON) therapy, with promising preliminary results ( NCT04123626 ). Autosomal dominant RP represents 15%–25% of all RP, withRHOaccounting for 20%–30% of these cases. Autosomal dominant macular and cone-rod dystrophies (MD/CORD) correspond to approximately 7.5% of all IRDs, and approximately 35% of all MD/CORD cases, with the main causative gene beingBEST1. Autosomal dominant IRDs are not only less frequent than recessive, but also tend to be less severe and have later onset; for example, an individual withRHO-adRP would typically become severely visually impaired at an age 2–3 times older than in X-linkedRPGR-RP. Gain-of-function and dominant negative aetiologies are frequently seen in the prevalent adRP genesRHO,RP1andPRPF31among others, which would not be effectively addressed by gene supplementation alone and need creative, novel approaches. Zinc fingers, RNA interference, AON, translational read-through therapy, and gene editing by clustered regularly interspaced short palindromic repeats/Cas are some of the strategies that are currently under investigation and will be discussed here.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".