Current Clinical Applications of In Vivo Gene Therapy with AAVs
Bibliographic record
Abstract
Hereditary diseases are caused by mutations in genes, and more than 7,000 rare diseases affect over 30 million Americans. For more than 30 years, hundreds of researchers have maintained that genetic modifications would provide effective treatments for many inherited human diseases, offering durable and possibly curative clinical benefit with a single treatment. This review is limited to gene therapy using adeno-associated virus (AAV) because the gene delivered by this vector does not integrate into the patient genome and has a low immunogenicity. There are now five treatments approved for commercialization and currently available, i.e., Luxturna, Zolgensma, the two chimeric antigen receptor T cell (CAR-T) therapies (Yescarta and Kymriah), and Strimvelis (the gammaretrovirus approved for adenosine deaminase-severe combined immunodeficiency [ADA-SCID] in Europe). Dozens of other treatments are under clinical trials. The review article presents a broad overview of the field of therapy by in vivo gene transfer. We review gene therapy for neuromuscular disorders (spinal muscular atrophy [SMA]; Duchenne muscular dystrophy [DMD]; X-linked myotubular myopathy [XLMTM]; and diseases of the central nervous system, including Alzheimer's disease, Parkinson's disease, Canavan disease, aromatic l-amino acid decarboxylase [AADC] deficiency, and giant axonal neuropathy), ocular disorders (Leber congenital amaurosis, age-related macular degeneration [AMD], choroideremia, achromatopsia, retinitis pigmentosa, and X-linked retinoschisis), the bleeding disorder hemophilia, and lysosomal storage disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".