MétaCan
Menu
Back to cohort

Structure–Activity and Cationic Amphiphilic Drug-like Behavior of Aromatic Triamino Glycosylated Antitumor Ether Lipids with Cytotoxicity in 2D and 3D Models

2025· article· en· W4416674533 on OpenAlexafffund
Rajat Arora, Megan Rodriguez, Gilbert Arthur, Mark W. Nachtigal, Frank Schweizer

Bibliographic record

VenueACS Medicinal Chemistry Letters · 2025
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsCancerCare ManitobaResearch Institute in Oncology and HematologyUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
KeywordsCytotoxicityAmphiphileDoxorubicinCationic polymerizationEtherCisplatinDrugViability assay

Abstract

fetched live from OpenAlex

This study reports the synthesis, cytotoxic evaluation, and mechanistic insights of an amphiphilic triamino glycosylated antitumor ether lipid (GAEL). A series of aryl-substituted tricationic d -galacto-GAELs were synthesized to mimic cationic amphiphilic drug (CAD)-like structural characteristics. Among the series, the quinoline-bearing triamino GAEL (compound 17 ) exhibited the highest cytotoxicity in 2D cultures against drug-sensitive and drug-resistant ovarian, breast, pancreatic, liver, prostate, and brain cancer cells, completely eliminating all cells, whereas cisplatin and doxorubicin were less effective. GAEL 17 also demonstrated superior efficacy in an SK-OV-3 3D tumor spheroid model, fully disintegrating spheroids and inducing cell death at concentrations ≥25 μM. In contrast, doxorubicin reduced viability but did not eradicate spheroids at 50 μM, likely due to slower drug action or limited penetration over 48 h exposure. GAEL 17 retained caspase-independent, non-apoptotic cell death. LysoTracker assay indicated lysosomal disruption, while LipidTOX staining showed dose-dependent fluorescence, consistent with CAD-like lipid accumulation.

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 score0.010
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.007
GPT teacher head0.234
Teacher spread0.227 · 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 routes2
Has abstractyes

Explore more

Same venueACS Medicinal Chemistry LettersSame topicSupramolecular Self-Assembly in MaterialsFrench-language works237,207