The N=1 Collaborative: advancing customized nucleic acid therapies through collaboration and data sharing
Bibliographic record
Abstract
Developing customized gene-targeting therapies for the millions of individuals affected by ultra-rare diseases globally requires breaking new ground in therapeutic and regulatory innovation. To address this need, the N=1 Collaborative (N1C) was established to unite academia, industry, patients, and regulators, building an open, shared ecosystem for personalized medicines. Initially focusing on antisense oligonucleotides (ASOs) for rare, fatal neurodegenerative conditions, the N1C aims to develop frameworks that can rapidly extend to other treatment modalities and conditions. Progress in the advancement of personalized therapies has also propelled advancements in the nucleic acids field, offering critical insights into dosing, safety, and efficacy. In October 2024, the N1C convened scientific, regulatory, and advocacy leaders in ASO development for an inaugural meeting. This review report examines the current state of the scientific and clinical ecosystems enabling customized genetic therapies and explores the innovation, frameworks, and systems needed to deliver additional individualized medicines safely and at scale.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".