Safety and Efficacy of Adeno-Associated Viral Gene Therapy in Patients With Retinal Degeneration: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Purpose: This systematic review evaluates the safety and efficacy of ocular gene therapy using adeno-associated virus (AAV). Methods: MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov were searched systematically for controlled or non-controlled interventional gene therapy studies using key words related to retinal diseases, gene therapy, and AAV vectors. The primary outcome measure was safety, based on ocular severe adverse events (SAEs). Secondary outcome measures evaluated efficacy of the therapy based on best corrected visual acuity (BCVA) and improvements in visual sensitivity and systemic involvement following ocular delivery. Pooling was done using a DerSimonian Laird random effects model. Risk of bias was assessed using the Cochrane Risk of Bias Tool, version 1. Results: Our search identified 3548 records. Of these, 80 publications met eligibility criteria, representing 28 registered clinical trials and 5 postmarket surveillance studies involving AAV gene therapy for Leber congenital amaurosis (LCA), choroideremia, Leber hereditary optic neuropathy (LHON), age-related macular degeneration (AMD), retinitis pigmentosa (RP), X-linked retinoschisis, and achromatopsia. Overall, AAV therapy vectors were associated with a cumulative incidence of at least one SAE of 8% (95% confidence intervals [CIs] of 5% to 12%). SAEs were often associated with the surgical procedure rather than the therapeutic vector itself. Poor or inconsistent reporting of adverse events (AEs) were a limitation for the meta-analysis. The proportion of patients with any improvement in BCVA and visual sensitivity was 41% (95% CIs of 31% to 51%) and 51% (95% CIs of 31% to 70%), respectively. Systemic immune involvement was associated with a cumulative incidence of 31% (95% CI = 21% to 42%). Conclusions: AAV gene therapy vectors appear to be safe but the surgical procedure required to deliver them is associated with some risk. The large variability in efficacy can be attributed to the small number of patients treated, the heterogeneity of the population and the variability in dosage, volume, and follow-up. Translational Relevance: This systematic review will help to inform and guide future clinical trials.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.027 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".