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Record W7135074353 · doi:10.1177/21593337251405312

Conjugated Antisense Oligonucleotides for Skipping of Duchenne Muscular Dystrophy Exon 53: A Cautionary Study

2025· article· en· W7135074353 on OpenAlexafffund
Emma T. Groenwold, Alicia Montulet, Tiberiu Loredan Stan, Diana McCorquodale, Nicholas McHugh, Dimas Echeverria, C. Tanganyika-de Winter, Maarten van del Wal, Daniel O’Reilly, Anastasia Khvorova, A. Aartsma-Rus, M. J. Damha

Bibliographic record

VenueNucleic Acid Therapeutics · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsExon skippingDuchenne muscular dystrophyExonOligonucleotideConjugateNucleic acidAntisense therapyAdverse effect

Abstract

fetched live from OpenAlex

Exon skipping antisense oligonucleotides (AONs) have been extensively studied as a promising method of treating Duchenne muscular dystrophy (DMD), yet the clinical efficacy of the conditionally approved AONs still remains low. Using phosphorothioated locked nucleic acid/2′-fluoro-RNA AONs, we aimed to increase AON efficiency by employing skeletal muscle-targeting conjugate molecules, cholesterol, and docosanoic acid to improve the biodistribution of the therapeutic. While conjugate molecules were able to induce high levels of skipping in an in vitro model, in vivo studies in the hDMDdel52/ mdx mouse model caused adverse symptomatic and systemic immune reactions, up to and including death, with little to no appreciable increase in exon skipping. Our study cautions against using these AON conjugates in an animal model due to severe toxicity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.013
GPT teacher head0.283
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2025
Admission routes2
Has abstractyes

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