Prospects for RNAi Therapy of COVID-19
Bibliographic record
Abstract
COVID-19 caused by the SARS-CoV-2 virus is a fast emerging disease with deadly consequences. The pulmonary system and lungs in particular are most prone to damage caused by the SARS-CoV-2 infection, which leaves a destructive footprint in the lung tissue, making it incapable of conducting its respiratory functions and resulting in severe acute respiratory disease and loss of life. There were no drug treatments or vaccines approved for SARS-CoV-2 at the onset of pandemic, necessitating an urgent need to develop effective therapeutics. To this end, the innate RNA interference (RNAi) mechanism can be employed to develop front line therapies against the virus. This approach allows specific binding and silencing of therapeutic targets by using short interfering RNA (siRNA) and short hairpin RNA (shRNA) molecules. In this review, we lay out the prospect of the RNAi technology for combatting the COVID-19. We first summarize current understanding of SARS-CoV-2 virology and the host response to viral entry and duplication, with the purpose of revealing effective RNAi targets. We then summarize the past experience with nucleic acid silencers for SARS-CoV, the predecessor for current SARS-CoV-2. Efforts targeting specific protein-coding regions within the viral genome and intragenomic targets are summarized. Emphasizing non-viral delivery approaches, molecular underpinnings of design of RNAi agents are summarized with comparative analysis of various systems used in the past. Promising viral targets as well as host factors are summarized, and the possibility of modulating the immune system are presented for more effective therapies. We place special emphasis on the limitations of past studies to propel the field faster by focusing on most relevant models to translate the promising agents to a clinical setting. Given the urgency to address lung failure in COVID-19, we summarize the feasibility of delivering promising therapies by the inhalational route, with the expectation that this route will provide the most effective intervention to halt viral spread. We conclude with the authors' perspectives on the future of RNAi therapeutics for combatting SARS-CoV-2. Since time is of the essence, a strong perspective for the path to most effective therapeutic approaches are clearly articulated by the authors.
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.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".