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Record W2990147736 · doi:10.1093/nar/gkz1115

Targeted delivery and enhanced gene-silencing activity of centrally modified folic acid–siRNA conjugates

2019· article· en· W2990147736 on OpenAlexafffund
Lidya Salim, Golam Islam, Jean‐Paul Desaulniers

Bibliographic record

VenueNucleic Acids Research · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyGene silencingFolic acidTransfectionGeneBiochemistryMolecular biologyInternal medicine

Abstract

fetched live from OpenAlex

One of the major hurdles in RNAi research has been the development of safe and effective delivery systems for siRNAs. Although various chemical modifications have been proposed to improve their pharmacokinetic behaviour, their delivery to target cells and tissues presents many challenges. In this work, we implemented a receptor-targeting strategy to selectively deliver siRNAs to cancer cells using folic acid as a ligand. Folic acid is capable of binding to cell-surface folate receptors with high affinity. These receptors have become important molecular targets for cancer research as they are overexpressed in numerous cancers despite being expressed at low levels in normal tissues. Employing a post-column copper-catalyzed alkyne-azide cycloaddition (CuAAC), we report the synthesis of siRNAs bearing folic acid modifications at different positions within the sense strand. In the absence of a transfection carrier, these siRNAs were selectively taken up by cancer cells expressing folate receptors. We show that centrally modified folic acid-siRNAs display enhanced gene-silencing activity against an exogenous gene target (∼80% knockdown after 0.75 μM treatment) and low cytotoxicity. In addition, these siRNAs achieved potent dose-dependent knockdown of endogenous Bcl-2, an important anti-apoptotic gene.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.025
GPT teacher head0.296
Teacher spread0.271 · 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 designBench or experimental
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

Citations54
Published2019
Admission routes2
Has abstractyes

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