Gene Replacement Therapy in Patients With Cardiac Disease: Challenges in Trial Design and Management of Adverse Events
Bibliographic record
Abstract
Gene replacement therapy has emerged as a promising strategy to address the underlying molecular defects in inherited and acquired cardiomyopathies, shifting treatment from symptom palliation to potentially disease-modifying interventions. Most clinical programs use adeno-associated viral vectors to deliver functional DNAs, demonstrating safety, durable myocardial transduction, and early improvements in biomarkers or imaging end points. However, the rarity and heterogeneity of target populations constrain trial size and duration, making traditional morbidity and mortality outcomes infeasible. Central to overcoming these challenges has been the concurrent establishment of rigourous natural history cohorts. They serve as external controls, allowing for the capture of exact disease trajectories to define the optimal effective therapeutic windows. Natural history studies are critical to identifying clinically meaningful surrogate end points, ranging from circulating biomarkers and quantitative imaging measures to composite functional ranks that integrate exercise capacity with patient-reported symptoms. Collaborating with regulatory authorities to identify composite outcomes that combine surrogate outcomes predictive of morbidity and mortality with innovative patient-reported outcomes, the obstacles of statistical power and hard outcomes can be overcome. Last, a comprehensive understanding of the immune response to viral capsids, together with optimized and validated immunosuppressive regimens, is much needed to deliver durable, disease-modifying therapies to patients with genetic cardiac diseases. Continued collaboration among investigators, regulators, and patient communities, including rigourous natural history study design, surrogate qualification, and innovative trial frameworks, will be essential to realize the full potential of gene replacement therapies in cardiology.
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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 | 0.401 | 0.377 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".