Upgrading nucleic acid and antisense therapeutics: challenges, solutions, and future directions
Bibliographic record
Abstract
Only a small fraction of disease-modifying proteins present druggable pockets for conventional small-molecule or biologic therapies, underscoring the urgent need for innovative strategies such as nucleic acid-based antisense therapeutics. Antisense approaches-including antisense oligonucleotides (ASOs), RNA interference (RNAi), and decoy oligodeoxynucleotides (ODNs)-offer powerful means to directly modulate gene expression at the RNA level. Over the past four decades, these modalities have advanced from early proof-of-concept studies to numerous FDA- and EMA-approved therapies for neuromuscular, metabolic, and neurodegenerative diseases. Despite these successes, critical barriers remain. Antisense drugs face challenges related to nuclease degradation, off-target binding, dose-dependent toxicities, limited tissue penetration, and inefficient endosomal escape. Addressing these limitations will require advances in nucleotide chemistry, conjugation strategies, and delivery platforms. Personalized "N-of-1" therapies further highlight the promise of customized oligonucleotides but also raise ethical and cost considerations. This review synthesizes the current state of antisense modalities, the obstacles impeding their broader application, and the innovative approaches needed to upgrade existing platforms and expand their therapeutic potential across a wider range of genetic and acquired diseases.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".