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Record W1985638596 · doi:10.2217/clp.14.27

Development and clinical applications of siRNA-encapsulated lipid nanoparticles in cancer

2014· article· en· W1985638596 on OpenAlexaff
Paulo J.C. Lin, Ying K. Tam, Pieter R. Cullis

Bibliographic record

VenueClinical Lipidology · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsAcuitas Therapeutics (Canada)University of British Columbia
Fundersnot available
KeywordsGene silencingSmall interfering RNACancerBiocompatible materialRNA interferenceMedicineClinical trialNucleic acidCancer researchBioinformaticsTransfectionChemistryBiologyCell cultureRNABiochemistryInternal medicineGeneticsGene

Abstract

fetched live from OpenAlex

Efficient delivery of siRNA to cancer cells after systemic administration poses a significant challenge. While many methods of nucleic acid delivery have been described, encapsulation of siRNA in lipid nanoparticles (LNPs) represents the most clinically advanced delivery approach. Currently, there are two siRNA-LNP-based treatments (ALN-VSP and TKM-PLK1) in clinical trials targeting solid tumors, with additional studies ongoing for noncancer diseases. The consensus from these clinical studies is that siRNA-LNP represents safe and potent silencing systems. Improvements in LNP technology through development of more potent and biocompatible ionizable cationic lipids along with targeting lipids to mediate delivery of LNPs specifically to cancer cells are on the horizon. In combination with genomic screening, it is possible that within the next 5 years the pathogenic drivers of individual cancers will be identified and siRNA-based personalized medicines will be formulated to achieve successful treatment of cancer and other genetic diseases.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.053
GPT teacher head0.379
Teacher spread0.326 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations19
Published2014
Admission routes1
Has abstractyes

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