Recent Developments in Delivery of Nucleic Acid-Based Antiviral Agents
Bibliographic record
Abstract
Rapid advances in viral genomics, gene function and regulation, as well as in rational drug design, have led to the development of gene-based drugs that can induce protective antiviral immunity, interfere with viral replication, suppress viral gene expression or cleave viral mRNAs. Several such drug candidates have been developed in recent years against various viruses including HIV. Although gene-based agents show promise as anti-viral agents their therapeutic efficacy may be restricted by limited delivery to intracellular sites of viral replication and in vivo nuclease degradation. Enhancement of the efficacy of gene-based drugs by encapsulation within liposomes or insertion within viral vectors has been evaluated. This review will highlight recent developments in delivery systems used to target nucleic acid-based drugs into sites of viral replication, therefore avoiding potential drug toxicity in non-viral infected organs. Liposome-encapsulation and insertion of nucleic acid-based drugs within viral vectors can significantly enhance antiviral efficacies. Viral vector-mediated therapy usually results in greater expression of the gene-based drug than with liposome delivery, however significant safety concerns have been raised in regards to viral vector therapies. Research is ongoing to increase drug delivery to the desired target cells while eliminating adverse side effects.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".