Optimal delivery of RNA interference by viral vectors for cancer therapy
Bibliographic record
Abstract
In recent years, there has been a surge in the innovative modification and application of the viral vector-based gene therapy field. Significant and consistent improvements in the engineering, delivery, and safety of viral vectors have set the stage for their application as RNA interference (RNAi) delivery tools. Viral vector-based delivery of RNAi has made remarkable breakthroughs in the treatment of several debilitating diseases and disorders (e.g., neurological diseases); however, their novelty has yet to be fully applied and utilized for the treatment of cancer. This review highlights the most promising and emerging viral vector delivery tools for RNAi therapeutics while discussing the variables limiting their success and suitability for cancer therapy. Specifically, we outline different integrating and non-integrating viral platforms used for gene delivery, currently employed RNAi targets for anti-cancer effect, and various strategies used to optimize the safety and efficacy of these RNAi therapeutics. Most importantly, we provide great insight into what challenges exist in their application as cancer therapeutics and how these challenges can be effectively navigated to advance the field.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".