Assays for the Identification and Prioritization of Drug Candidates for Spinal Muscular Atrophy
Bibliographic record
Abstract
Spinal muscular atrophy (SMA) is an autosomal recessive genetic disorder resulting in degeneration of α-motor neurons of the anterior horn and proximal muscle weakness. It is the leading cause of genetic mortality in children younger than 2 years. It affects ∼1 in 11,000 live births. In 95% of cases, SMA is caused by homozygous deletion of the SMN1 gene. In addition, all patients possess at least one copy of an almost identical gene called SMN2. A single point mutation in exon 7 of the SMN2 gene results in the production of low levels of full-length survival of motor neuron (SMN) protein at amounts insufficient to compensate for the loss of the SMN1 gene. Although no drug treatments are available for SMA, a number of drug discovery and development programs are ongoing, with several currently in clinical trials. This review describes the assays used to identify candidate drugs for SMA that modulate SMN2 gene expression by various means. Specifically, it discusses the use of high-throughput screening to identify candidate molecules from primary screens, as well as the technical aspects of a number of widely used secondary assays to assess SMN messenger ribonucleic acid (mRNA) and protein expression, localization, and function. Finally, it describes the process of iterative drug optimization utilized during preclinical SMA drug development to identify clinical candidates for testing in human clinical trials.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".