MétaCan
Menu
Back to cohort
Record W4401653289 · doi:10.1002/adma.202403701

Ionizable Drugs Enable Intracellular Delivery of Co‐Formulated siRNA

2024· article· en· W4401653289 on OpenAlexafffund
Kai V. Slaughter, Eric N. Donders, Michael S. Jones, Sami G. Sabbah, Mitchell J. Elliott, Brian K. Shoichet, David W. Cescon, Molly S. Shoichet

Bibliographic record

VenueAdvanced Materials · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of TorontoNatural Sciences and Engineering Research Council of CanadaAmgen
KeywordsMaterials scienceIntracellularDrug deliveryNanotechnologyBiophysicsBiochemistryChemistryBiology

Abstract

fetched live from OpenAlex

Targeting complementary pathways in diseases such as cancer can be achieved with co-delivery of small interfering ribonucleic acid (siRNA) and small molecule drugs; however, current formulation strategies are typically limited to one, but not both. Here, ionizable small molecule drugs and siRNA are co-formulated in drug-rich nanoparticles. Ionizable analogs of the selective estrogen receptor degrader fulvestrant self-assemble into colloidal drug aggregates and cause endosomal disruption, allowing co-delivery of siRNA against a non-druggable target. siRNA is encapsulated in lipid-stabilized, drug-rich colloidal nanoparticles where the ionizable lipid used in conventional lipid nanoparticles is replaced with an ionizable fulvestrant analog. The selection of an appropriate phospholipid and formulation buffer enables endocytosis and potent reporter gene knockdown in cancer cells. Importantly, siRNA targeting cyclin E1 is effectively delivered to drug-resistant breast cancer cells, demonstrating the utility of this approach. This strategy opens the possibility of using ionizable drugs to co-deliver RNA and ultimately improve therapeutic outcomes.

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 score0.052
Threshold uncertainty score0.574

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 head0.006
GPT teacher head0.247
Teacher spread0.241 · 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

Citations14
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueAdvanced MaterialsSame topicRNA Interference and Gene DeliveryFrench-language works237,207