Developing an Approach to Screening Rare Genetic Diagnoses for Amenability to Bespoke Antisense Oligonucleotide Therapy Development
Bibliographic record
Abstract
Rare genetic conditions are major contributors to paediatric morbidity and mortality, but few have specialized treatments. Proof-of-concept exists for precision genetic therapies like antisense oligonucleotides (ASOs) that are customized for an individual’s specific genetic variant and/or ultra-rare condition. However, there are no consensus recommendations for how to triage diagnoses and variants within bespoke ASO programs. With support from the N=1 Collaborative (n1collaborative.org), we developed, piloted, and refined workflows for evaluating variant amenability to four ASO approaches: canonical exon skipping, splice correction, mRNA knockdown, and wildtype upregulation. Through the input of multiple experts, we arrived at a comprehensive and concise analysis approach. We then retrospectively applied these workflows to 341 diagnoses made by clinical genome-wide sequencing at SickKids in Toronto, Canada over a 4-year period. 20 (5.9%) were identified as being likely eligible or eligible for ASO therapy development.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
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".