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Record W4408865117 · doi:10.1126/sciadv.adt9354

Retinal gene therapy for Stargardt disease with dual AAV intein vectors is both safe and effective in large animal models

2025· article· en· W4408865117 on OpenAlexaff
Rita Ferla, Eugenio Pugni, Mariangela Lupo, Paola Tiberi, Federica Fioretto, Andrea Perota, Roberto Duchi, Irina Lagutina, Carlo Gesualdo, Settimio Rossi, Domenico Ventrella, Alberto Elmi, Benjamin McClinton, Carmel Toomes, Tongzhou Xu, Robert S. Molday, Enrico Maria Surace, Francesca Simonelli, Maria Laura Bacci, Cesare Galli, Muhammad Ali Memon, Naveed Shams, Alberto Auricchio, Ivana Trapani

Bibliographic record

VenueScience Advances · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsStargardt diseaseInteinGenetic enhancementTransduction (biophysics)BiologyRetinalGeneGeneticsRNA

Abstract

fetched live from OpenAlex

Retinal gene therapy using dual adeno-associated viral (AAV) intein vectors can be applied to genetic forms of blindness caused by mutations in genes with coding sequences that exceed single AAV cargo capacity, such as Stargardt disease (STGD1), the most common inherited macular dystrophy. In view of clinical translation of dual AAV intein vectors, here we set to evaluate both the efficiency and safety of their subretinal administration in relevant large animal models. Accordingly, we have developed the first pig model of STGD1, which we found to accumulate lipofuscin similarly to patients. This accumulation is significantly reduced upon subretinal administration of dual AAV intein vectors whose safety and pharmacodynamics we then tested in nonhuman primates, which showed modest and reversible inflammation as well as high levels of photoreceptor transduction. This bodes well for further clinical translation of dual AAV intein vectors in patients with STGD1 as well as for other blinding diseases that require the delivery of large genes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.267
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2025
Admission routes1
Has abstractyes

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