Endosomal Escape and Nuclear Localization: Critical Barriers for Therapeutic Nucleic Acids
Bibliographic record
Abstract
Therapeutic nucleic acids (TNAs) including antisense oligonucleotides (ASOs) and small interfering RNA (siRNA) have emerged as promising treatment strategies for a wide variety of diseases, offering the potential to modulate gene expression with a high degree of specificity. These small, synthetic nucleic acid-like molecules provide unique advantages over traditional pharmacological agents, including the ability to target previously "undruggable" genes. Despite this promise, several biological barriers severely limit their clinical efficacy. Upon administration, TNAs primarily enter cells through endocytosis, becoming trapped inside membrane-bound vesicles known as endosomes. Studies estimate that only 1-2% of TNAs successfully escape endosomal compartments to reach the cytosol, and in some cases the nucleus, where they bind target mRNA and exert their therapeutic effect. Endosomal entrapment and inefficient nuclear localization are therefore critical bottlenecks in the therapeutic application of TNAs. This review explores the current understanding of TNA endosomal escape and nuclear transport along with strategies aimed at overcoming these challenges, including the use of endosomal escape agents, peptide-TNA conjugates, non-viral delivery vehicles, and nuclear localization signals. By improving both endosomal escape and nuclear localization, significant advances in TNA-based therapeutics can be realized, ultimately expanding their clinical utility.
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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) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".