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Record W2008650353 · doi:10.1158/1078-0432.mechres-b32

Abstract B32: Impact of Lipid-Substitution on Assembly and Delivery of siRNA by Cationic Polymers

2012· article· en· W2008650353 on OpenAlexaff
Hamidreza Montazeri Aliabadi, Hasan Uludağ

Bibliographic record

VenueClinical Cancer Research · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTransfectionSmall interfering RNAPolymerChemistryIn vitroBiochemistryCationic polymerizationIn vivoCombinatorial chemistryBiologyGeneOrganic chemistryBiotechnology

Abstract

fetched live from OpenAlex

Abstract Downregulation of target protein expression by small interfering RNA (siRNA) has been one of the most promising strategies in medicinal therapy since the discovery of this revolutionary process in late 1990s. Many obstacles, however, have to be overcome to achieve safe and efficient siRNA delivery to target cells. Short in vivo half-life of siRNA and safety concerns involved with viral vectors used for DNA transfection are among the main reasons for the necessity of a better delivery system to take full advantage of this strategy. Polycationic polymers have been studied extensively for this purpose and high molecular weight polyethylenimines (PEIs) have led to promising results; however, unacceptable toxicity profile of these polymers has been a hurdle for their clinical application. In this study, we report characterization of a library of polymers synthesized by hydrophobic modification of a low molecular weight (2 kDa) PEI (PEI2) with a wide range of different fatty acids, engineered to enhance their ability to protect and deliver their nucleotide cargo to the cells, while still being water soluble. A general increase in lipid substitution was observed as the lipid:PEI ratio was increased during the synthesis. Among the polymers derived from lipid:PEI ratio of 0.066, caprylic acid-substituted polymer showed a lower binding affinity, while all other polymers performed similarly. Complete siRNA binding was typically achieved at polymer:siRNA ratio of < 0.5. The BC50 (polymer:siRNA ratio needed for 50% binding) was generally increased with the extent of lipid substitution, indicating an adverse effect of lipid substitution on siRNA complexation. Particle size analysis showed a range of 300 to 600 nm for all of the polymer/siRNA complexes, and for all lipid-substituted polymers, a continuous increase in the ζ-potential was observed with increasing polymer:siRNA weight ratio, and all polymers showed positive ζ-potential at the ratio of 10:1. The ζ-potentials of complexes formed with lipid-modified polymers were higher than complexes of PEI2 at all ratios. With the polymer:siRNA ratio of 1:1, all lipid-substituted polymers showed complete protection against degradation, while naked siRNA was readily degraded (<5% intact siRNA remaining) and only ∼68% of siRNA bound with PEI2 remained intact under the experimental conditions. The cytotoxicity of the synthesized polymers was slightly higher than PEI2, but significantly lower than larger MW PEI. Polymer/siRNA complexes formed with lipid-modified polymers increased the cellular uptake of siRNA significantly. Down-regulation of two different target proteins was also evaluated using the modified polymers: a housekeeping enzyme glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and an efflux protein involved in multi-drug resistance (MDR), P-glycoprotein (P-gp). Optimum silencing for the target proteins was achieved by using polymer:siRNA ratio of 4:1 for GAPDH and 8:1 for P-gp. A maximum of 66% and 67% downregulation was observed for GAPDH and P-gp, respectively. Our results indicate that hydrophobic modification of low molecular PEI could render this otherwise ineffective polymer to a safe effective delivery system for intracellular siRNA delivery and silencing of protein expression.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.133
GPT teacher head0.489
Teacher spread0.356 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2012
Admission routes1
Has abstractyes

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