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Record W4224219978 · doi:10.1080/14712598.2022.2066471

Onasemnogene abeparvovec for the treatment of spinal muscular atrophy

2022· article· en· W4224219978 on OpenAlexaff
Hugh J. McMillan, Crystal M. Proud, Michelle A. Farrar, Ian E. Alexander, Francesco Muntoni, Laurent Servais

Bibliographic record

VenueExpert Opinion on Biological Therapy · 2022
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsMcGill University Health CentreMontreal Children's Hospital
Fundersnot available
KeywordsSpinal muscular atrophyMedicinePhysical medicine and rehabilitationPathologyDisease

Abstract

fetched live from OpenAlex

Introduction Gene therapy for spinal muscular atrophy (SMA) represents a significant milestone in the treatment of neurologic diseases. SMA is a neurodegenerative disease that results in motor neuron loss because of mutations of the survival motor neuron 1 gene, which directs survival motor neuron (SMN) protein production. Onasemnogene abeparvovec, a one-time gene replacement therapy, delivers a functional transgene to restore SMN protein expression. Onasemnogene abeparvovec has demonstrated improved survival and motor milestone achievements for presymptomatic infants and patients with SMA type 1.Areas covered This expert review describes the current state of gene therapy for SMA, reviews the mechanism of and clinical experience with onasemnogene abeparvovec, explains future efforts to expand applications of gene therapy for SMA, and provides context for developing gene therapy for other conditions.Expert opinion Onasemnogene abeparvovec has demonstrated efficacy in clinical trials and, because of this, is a valuable treatment option for patients with symptomatic infantile SMA and those identified by newborn screening. Gene therapy is still in its infancy, and challenges and uncertainties associated with transgene delivery must be addressed. With ongoing development of vector technology, more specific tissue tropism, reduced ‘off-target’ effects, and an enhanced safety profile will continue to evolve.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.943
Threshold uncertainty score0.707

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)0.0010.000

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 head0.159
GPT teacher head0.406
Teacher spread0.247 · 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 designNot applicable
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

Citations34
Published2022
Admission routes1
Has abstractyes

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