MétaCan
Menu
Back to cohort
Record W4322503763 · doi:10.3390/pharmaceutics15030778

Recent Trends in Antisense Therapies for Duchenne Muscular Dystrophy

2023· review· en· W4322503763 on OpenAlexafffund
Harry Wilton-Clark, Toshifumi Yokota

Bibliographic record

VenuePharmaceutics · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsUniversity of Alberta
FundersDefeat Duchenne CanadaCanadian Institutes of Health ResearchMuscular Dystrophy CanadaUniversity of AlbertaAlberta Innovates - Health SolutionsWomen and Children's Health Research InstituteChildren's Health Research InstituteAlberta InnovatesU.S. Department of Defense
KeywordsDuchenne muscular dystrophyAntisense therapyExon skippingMuscular dystrophyMedicineGenetic enhancementDiseaseClinical trialDystrophinDrug developmentBioinformaticsExonDrugGeneBiologyGeneticsPharmacologyInternal medicineAlternative splicing

Abstract

fetched live from OpenAlex

Duchenne muscular dystrophy (DMD) is a debilitating and fatal genetic disease affecting 1/5000 boys globally, characterized by progressive muscle breakdown and eventual death, with an average lifespan in the mid-late twenties. While no cure yet exists for DMD, gene and antisense therapies have been heavily explored in recent years to better treat this disease. Four antisense therapies have received conditional FDA approval, and many more exist in varying stages of clinical trials. These upcoming therapies often utilize novel drug chemistries to address limitations of existing therapies, and their development could herald the next generation of antisense therapy. This review article aims to summarize the current state of development for antisense-based therapies for the treatment of Duchenne muscular dystrophy, exploring candidates designed for both exon skipping and gene knockdown.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.153
GPT teacher head0.443
Teacher spread0.290 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations49
Published2023
Admission routes2
Has abstractyes

Explore more

Same venuePharmaceuticsSame topicMuscle Physiology and DisordersFrench-language works237,207