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Record W2134917687 · doi:10.2174/157489107780832622

Recent Patents on Development of Nucleic Acid-based Antiviral Drugs against Seasonal and Pandemic Influenza Virus Infections

2007· review· en· W2134917687 on OpenAlexaff
Edward G. Saravolac, Jonathan P. Wong

Bibliographic record

VenueRecent Patents on Anti-Infective Drug Discovery · 2007
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsNeuraminidaseOseltamivirPandemicVirologyInfluenza A virus subtype H5N1VirusNucleic acidZanamivirInfluenza A virusBiologyMedicineCoronavirus disease 2019 (COVID-19)DiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Influenza viruses are etiological agents of deadly flu that continue to pose global health threats, and have caused global pandemics that killed millions of people worldwide. The global crisis involving the avian H5N1 influenza provides compelling reasons to accelerate fast track development of novel antiviral drugs against the potential pandemic virus. The availability of neuraminidase inhibitors such as oseltamivir (tamiflu) improves our ability to defend against influenza viruses, but the incidences of tamiflu-resistance are rising rapidly. Nucleic acid-based antiviral drugs are promising classes of experimental antiviral drugs that have been shown in pre-clinical studies to be effective against seasonal and avian influenza viruses. The potency and versatility of these drugs make them potential candidates to be used in seasonal and pandemic influenza scenarios. The review will assess the recent patents, research and development of antisense oligonucleotides, small interfering RNA, immunomodulating RNA for the prevention and treatment of influenza infection.

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)0.001
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: none
Teacher disagreement score0.982
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.052
GPT teacher head0.334
Teacher spread0.281 · 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

Citations6
Published2007
Admission routes1
Has abstractyes

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