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Record W1983959282 · doi:10.2174/138161206777442146

Recent Developments in Delivery of Nucleic Acid-Based Antiviral Agents

2006· review· en· W1983959282 on OpenAlexaff
Mary E. Christopher, Jonathan P. Wong

Bibliographic record

VenueCurrent Pharmaceutical Design · 2006
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsMedicine Hat College
Fundersnot available
KeywordsViral vectorNucleic acidViral replicationBiologyGene deliveryAntiviral drugGenetic enhancementDrug deliveryNucleaseVirologyGeneVirusChemistryBiochemistryRecombinant DNA

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.190
GPT teacher head0.418
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations20
Published2006
Admission routes1
Has abstractyes

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