Overcoming the Challenges to Clinical Development of X-Linked Retinitis Pigmentosa Therapies: Proceedings of an Expert Panel
Bibliographic record
Abstract
X-linked retinitis pigmentosa (XLRP) is a rare inherited retinal disease manifesting as impaired night vision and peripheral vision loss that progresses to legal blindness. Although several trials of ocular gene therapy for XLRP have been conducted or are in progress, there is currently no approved treatment. In July 2022, the Foundation Fighting Blindness convened an expert panel to examine relevant research and make recommendations for overcoming the challenges and capitalizing on the opportunities in conducting clinical trials of RPGR-targeted therapy for XLRP. Data presented concerned RPGR structure and mutation types known to cause XLRP, RPGR mutation-associated retinal phenotype diversity, patterns in genotype/phenotype relationships, disease onset and progression from natural history studies, and the various functional and structural tests used to monitor disease progression. Panel recommendations include considerations, such as genetic screening and other factors that can impact clinical trial inclusion criteria, the influence of age on defining and stratifying participant cohorts, the importance of conducting natural history studies early in clinical development programs, and the merits and drawbacks of available tests for measuring treatment outcomes. We recognize the need to work with regulators to adopt clinically meaningful end points that would best determine the efficacy of a trial. Given the promise of RPGR-targeted gene therapy for XLRP and the difficulties encountered in phase III clinical trials to date, we hope these recommendations will help speed progress to finding a cure. Translational Relevance: Examination of relevant data and recommendations for the successful clinical development of gene therapies for RPGR-associated XLRP.
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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 | 0.197 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.032 | 0.038 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".