Unlocking the Potential of Chemically Modified Nucleic Acid Therapeutics
Bibliographic record
Abstract
Abstract Nucleic acid therapeutics have demonstrated tremendous potential for treating diseases by targeting the genetic underpinnings at the transcriptomic level. However, their efficacy hinges on robust strategies to protect nucleic acids from degradation during circulation and to facilitate precise delivery to diseased tissues and cells. Here the critical roles of chemical modification and bioconjugation in advancing nucleic acid therapeutics for improved binding affinity, enhanced stability, and targeted delivery are reviewed. Commencing diverse applications, the significance of different chemical modifications is discussed based on recent literature and clinical products, on oligonucleotides. These modifications encompass backbone, ribose, base alterations and bioconjugation techniques such as N ‐acetylgalactosamine (GAlNac), aptamers, antibodies, and cell‐penetrating peptides (CPPs). Supported by a clinical perspective, diverse applications and ongoing developments are highlighted. Furthermore, the current landscape of nucleic acid therapeutics and their potential in addressing genetic disorders with multiple cellular/organelle targeting is discussed. Here the promising prospect of combining chemical innovation and bioconjugation strategies is underscored to propel the development of more effective nucleic acid therapeutics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".