Potential of siRNA in COVID-19 therapy: Emphasis on in silico design and nanoparticles based delivery
Bibliographic record
Abstract
Small interfering RNA (siRNA)-mediated mRNA degradation approach have imparted its eminence against several difficult-to-treat genetic disorders and other allied diseases. Viral outbreaks and resulting pandemics have repeatedly threatened public health and questioned human preparedness at the forefront of drug design and biomedical readiness. During the recent pandemic caused by the SARS-CoV-2, mRNA-based vaccination strategies have paved the way for a new era of RNA therapeutics. RNA Interference (RNAi) based approach using small interfering RNA may complement clinical management of the COVID-19. RNA Interference approach will primarily work by restricting the synthesis of the proteins required for viral replication, thereby hampering viral cellular entry and trafficking by targeting host as well as protein factors. Despite promising benefits, the stability of small interfering RNA in the physiological environment is of grave concern as well as site-directed targeted delivery and evasion of the immune system require immediate attention. In this regard, nanotechnology offers viable solutions for these challenges. The review highlights the potential of small interfering RNAs targeted toward specific regions of the viral genome and the features of nanoformulations necessary for the entrapment and delivery of small interfering RNAs. In silico design of small interfering RNA for different variants of SARS-CoV-2 has been discussed. Various nanoparticles as promising carriers of small interfering RNAs along with their salient properties, including surface functionalization, are summarized. This review will help tackle the real-world challenges encountered by the in vivo delivery of small interfering RNAs, ensuring a safe, stable, and readily available drug candidate for efficient management of SARS-CoV-2 in the future.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".