MétaCan
Menu
Back to cohort
Record W4417215002 · doi:10.1021/acs.biomac.5c01884

Antitumor Activity of Death Receptor 5-Targeted Camptothecin-Loaded Nanoparticles in Murine Syngeneic Models

2025· article· en· W4417215002 on OpenAlexfundno aff
Anna Boland, Michelle K. Greene, Una M. Herron, Michael C. Johnston, Peter Smyth, H Yagita, Daniel B. Longley, Christopher J. Scott

Bibliographic record

VenueBiomacromolecules · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell death mechanisms and regulation
Canadian institutionsnot available
FundersMedical Research CouncilQueen's UniversityQueen's University Belfast
KeywordsNanoparticleMediatorApoptosisProgrammed cell deathReceptorAntibodyImmunogenic cell deathCell cultureDownregulation and upregulation

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Death receptor 5 (DR5) is a key mediator of the extrinsic apoptotic pathway that is often upregulated in tumors, rendering it an attractive target for cancer therapy. Activation of DR5 requires oligomerization, which can be achieved through multivalent presentation of DR5 ligands on nanoparticles. DR5-targeted nanoparticles can efficiently agonize DR5 to inhibit the growth of human xenografts, although it remains unclear whether these effects would translate to a syngeneic tumor model with an immunocompetent microenvironment. Here, we develop camptothecin-loaded polymeric nanoparticles coated with the murine DR5 antibody MD5–1 and demonstrate their pro-apoptotic effects in murine cell lines in vitro . Moreover, we show that these nanoparticles inhibit the growth of MC38 colorectal allografts in vivo by >90% relative to control nanoparticles. Collectively, our work confirms that the antitumor efficacy of DR5-targeted nanoparticles extends to syngeneic models, paving the way for future studies to explore their impact on tumor immunity and the surrounding microenvironment.

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 score0.006
Threshold uncertainty score0.629

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 head0.011
GPT teacher head0.249
Teacher spread0.238 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueBiomacromoleculesSame topicCell death mechanisms and regulationFrench-language works237,207