Overcoming the Innate Immune Response to Small Interfering RNA
Bibliographic record
Abstract
Many types of nucleic acid, including canonical small interfering RNA (siRNA) duplexes, are potent activators of the mammalian innate immune system. Synthetic siRNA duplexes can induce high levels of inflammatory cytokines and type I interferons, in particular interferon-alpha, after systemic administration in mammals and in primary human blood cell cultures. These responses are greatly potentiated by the use of delivery vehicles that facilitate cellular uptake of the siRNA. Although the immunomodulatory effects of nucleic acids may be harnessed therapeutically, for example, in oncology and allergy applications, in many cases immune activation represents a significant undesirable side effect due to the toxicities associated with excessive cytokine release and associated inflammatory syndromes. The potential for siRNA-based drugs to be rendered immunogenic is also a cause for concern because the establishment of an antibody response may severely compromise both safety and efficacy. Clearly, there are significant implications both for the development of siRNA-based drugs and in the interpretation of gene-silencing effects elicited by siRNA. This review provides the background information required to anticipate, manage, and abrogate the immunological effects of siRNA and will assist the reader in the successful in vivo application of siRNA-based drugs.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".