Innovative developments and emerging technologies in RNA therapeutics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
RNA-based therapeutics are emerging as a powerful platform for the treatment of multiple diseases. Currently, the two main categories of nucleic acid therapeutics, antisense oligonucleotides and small interfering RNAs (siRNAs), achieve their therapeutic effect through either gene silencing, splicing modulation or microRNA binding, giving rise to versatile options to target pathogenic gene expression patterns. Moreover, ongoing research seeks to expand the scope of RNA-based drugs to include more complex nucleic acid templates, such as messenger RNA, as exemplified by the first approved mRNA-based vaccine in 2020. The increasing number of approved sequences and ongoing clinical trials has attracted considerable interest in the chemical development of oligonucleotides and nucleic acids as drugs, especially since the FDA approval of the first siRNA drug in 2018. As a result, a variety of innovative approaches is emerging, highlighting the potential of RNA as one of the most prominent therapeutic tools in the drug design and development pipeline. This review seeks to provide a comprehensive summary of current efforts in academia and industry aimed at fully realizing the potential of RNA-based therapeutics. Towards this, we introduce established and emerging RNA-based technologies, with a focus on their potential as biosensors and therapeutics. We then describe their mechanisms of action and their application in different disease contexts, along with the strengths and limitations of each strategy. Since the nucleic acid toolbox is rapidly expanding, we also introduce RNA minimal architectures, RNA/protein cleavers and viral RNA as promising modalities for new therapeutics and discuss future directions for the field.
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.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it