The current landscape of nucleic acid therapeutics
Bibliographic record
Abstract
The increasing number of approved nucleic acid therapeutics demonstrates the potential to treat diseases by targeting their genetic blueprints in vivo. Conventional treatments generally induce therapeutic effects that are transient because they target proteins rather than underlying causes. In contrast, nucleic acid therapeutics can achieve long-lasting or even curative effects via gene inhibition, addition, replacement or editing. Their clinical translation, however, depends on delivery technologies that improve stability, facilitate internalization and increase target affinity. Here, we review four platform technologies that have enabled the clinical translation of nucleic acid therapeutics: antisense oligonucleotides, ligand-modified small interfering RNA conjugates, lipid nanoparticles and adeno-associated virus vectors. For each platform, we discuss the current state-of-the-art clinical approaches, explain the rationale behind its development, highlight technological aspects that facilitated clinical translation and provide an example of a clinically relevant genetic drug. In addition, we discuss how these technologies enable the development of cutting-edge genetic drugs, such as tissue-specific nucleic acid bioconjugates, messenger RNA and gene-editing therapeutics. This Review provides an overview of four platform technologies that are currently used in the clinic for delivery of nucleic acid therapeutics, describing their properties, discussing technical advancements that led to clinical approval, and highlighting examples of approved genetic drugs that make use of these technologies.
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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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".