Assembling the RNA therapeutics toolbox
Bibliographic record
Abstract
From the approval of COVID-19 mRNA vaccines to the 2023 Nobel Prize awarded for nucleoside base modifications, RNA therapeutics have entered the spotlight and are transforming drug development. While the term "RNA therapeutics" has been used in various contexts, this review focuses on treatments that utilize RNA as a component or target RNA for therapeutic effects. We summarize the latest advances in RNA-targeting tools and RNA-based technologies, including but not limited to mRNA, antisense oligos, siRNAs, small molecules and RNA editors. We focus on the mechanisms of current FDA-approved therapeutics but also provide a discussion on the upcoming workforces. The clinical utility of RNA-based therapeutics is enabled not only by the advances in RNA technologies but in conjunction with the significant improvements in chemical modifications and delivery platforms, which are also briefly discussed in the review. We summarize the latest RNA therapeutics based on their mechanisms and therapeutic effects, which include expressing proteins for vaccination and protein replacement therapies, degrading deleterious RNA, modulating transcription and translation efficiency, targeting noncoding RNAs, binding and modulating protein activity and editing RNA sequences and modifications. This review emphasizes the concept of an RNA therapeutic toolbox, pinpointing the readers to all the tools available for their desired research and clinical goals. As the field advances, the catalog of RNA therapeutic tools continues to grow, further allowing researchers to combine appropriate RNA technologies with suitable chemical modifications and delivery platforms to develop therapeutics tailored to their specific clinical challenges.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".